Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offis...Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.展开更多
The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographica...The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.展开更多
Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still inv...Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.展开更多
The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of w...The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of wear zone of each end mill were analyzed, and three tool wear criterions of small-diameter end mills were defined. With the uEye camera, macro lens and 3-axis micro milling machine, it was proved the feasibility of measuring flank wear with the milling tests on a 45# steel workpiece. The design of experiment (DOE) showed that Vc was the most remarkable effect factor for the flank wear of small-diameter end mill. The wear curve of the experiments of milling was very similar to the Taylor curve.展开更多
This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) ...This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span><span><span><span> color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R</span><sup><span>2</span></sup><span> = 0.97), followed by the b* (R</span><sup><span>2</span></sup><span> = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.展开更多
The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image ar...The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image are taken as research objects. On the base of the traditional checking methods of printing quality,combining the method and theory of digital image processing with printing theory in the new domain of image quality checking,it constitute the checking system of printing quality by image processing,and expound the theory design and the model of this system. This is an application of machine vision. It uses the high resolution industrial CCD(Charge Coupled Device) colorful camera. It can display the real-time photographs on the monitor,and input the video signal to the image gathering card,and then the image data transmits through the computer PCI bus to the memory. At the same time,the system carries on processing and data analysis. This method is proved by experiments. The experiments are mainly about the data conversion of image and ink limit show of printing.展开更多
Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener...Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.展开更多
Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizati...Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.展开更多
To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focuse...To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.展开更多
Tundish-covering flux bags can be depalletized and moved in the steel casting region using industrial robots and monocular vision simultaneously.An industrial robot mounted with a flexible vacuum sucker was used as th...Tundish-covering flux bags can be depalletized and moved in the steel casting region using industrial robots and monocular vision simultaneously.An industrial robot mounted with a flexible vacuum sucker was used as the executor.For a structured bag model,a visual scheme based on the support vector machine and the histogram of oriented gradients was adopted.The computer was trained using a number of sample bag images that relied on the feature recognition algorithm.Finally,the automatic stacking and moving of the flux bags were realized.展开更多
This paper describes industrial sorting system, which is based on robot vision technology, introduces main image processing methodology used during development, and simulates algorithm with Matlab. Besides, we set up ...This paper describes industrial sorting system, which is based on robot vision technology, introduces main image processing methodology used during development, and simulates algorithm with Matlab. Besides, we set up image processing algorithm library via C# program and realize recognition and location for regular geometry workpiece. Furthermore, we analyze camera model in vision algorithm library, calibrate the camera, process the image series, and resolve the identify problem for regular geometry workpiece with different colours.展开更多
Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and th...Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.展开更多
In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation(PAR)(A-TEP), aiming to explore the relationship between muskmelon(Cucumis mel...In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation(PAR)(A-TEP), aiming to explore the relationship between muskmelon(Cucumis melo L.) fruit attributes and environmental factors. Muskmelon surface color was described by parameters of red, green, blue, hue, saturation and brightness(HSI). Three characteristic parameters, gray level co-occurrence matrix(GLCM), angular second moment(ASM), entropy, contrast, and the coverage rate were used to describe the process of muskmelon fruit netting formation. ASM was not significant difference during muskmelon fruit growth. The number and deep of netting stripes gradually increased with fruit growth. Coverage rate increased rapidly for 15–30 d after pollination. The vertical and horizontal diameters of muskmelon fruit were followed a logistic curve. And root mean squared errors(RMSE) between the simulated and measured vertical and horizontal diameters were 3.527 and 4.696 mm, respectively. RMSE of red, green, blue, saturation and brightness were 0.999, 2.690, 2.992, 0.033 and 5.51, respectively, and the RMSE for entropy, contrast and coverage rates were 0.077, 0.063 and 0.015, respectively, indicating a well consistent between measured and simulated values.展开更多
A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402,Shanyou 10, Zhongyou207, Jiayou and Ilyou were evaluated. The images of both sides of rice seed with black backgr...A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402,Shanyou 10, Zhongyou207, Jiayou and Ilyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.展开更多
This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds.Past research has shown that seed vigor is significantly related to the seed color and size,thus severa...This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds.Past research has shown that seed vigor is significantly related to the seed color and size,thus several physical features were identified as candidate predictors of high seed quality.Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101.In addition,binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination.Single-kernel germination tests were conducted to validate the predictive value of the identified features.The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds.Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features:three color features(R,a*,brightness),width,length,projected area,and single-kernel density,and weight.In contrast,fresh weight significantly negatively correlated with the feature of hue.In analyses of two of the highest correlating single features,germination percentage increased from 59.3 to 71.8% when a*≥3,and selection rate peaked at 57.8%.Germination percentage increased from 59.3 to 79.4%,and the selection rate reached 76.8%,when single-kernel weight≥0.0064 g.The most effective model was based on a multilayer perceptron(MLP)neural network,consisting of 15 physical traits as variables,and a stability calculated as 99.4%.Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%.These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection.Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.展开更多
When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of mate...When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB? environment. Im- ages are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.展开更多
In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionali...In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.展开更多
To improve the identification for visual defect of TFT-LCD,a new machine vision system is proposed,which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LC...To improve the identification for visual defect of TFT-LCD,a new machine vision system is proposed,which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing,such as average filtering and geometric correction,was performed on the captured image,and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast,objective and accurate inspection compared with subjective and inaccurate human eye inspection.展开更多
With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wo...With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable "defecs" that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.展开更多
Automatic visual inspection of fabric is not only one of the potential application of machinevision but a considerable challenge in textile engineering as well.This paper mainly discusses howto inspect fabric defects ...Automatic visual inspection of fabric is not only one of the potential application of machinevision but a considerable challenge in textile engineering as well.This paper mainly discusses howto inspect fabric defects using machine vision.The introduced inspection system has a feature of:(?)Categorizing the fabric defects into 4 groups,for each group diffcrent image processing and recog-nizing methods are designed for fast and efficient inspection:2.The inspection and recognitionparameters are determined by training and self learning,these parameters vary with different kindsof fabric;3.Human inspetor’s experiences are summed up as rules to ensure the system has a s(?)lar evaluation performance of human inspector.This system can detect most of the fab(?) defects.the total recognition error is less than 5% except for the detection error of yarn irregularity,whichcould be as high as 20%.展开更多
基金supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (NRF-2020R1I1A3073313).
文摘Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.
文摘The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.
基金Supported by the Fundamental Public Welfare Research Program of Zhejiang Provincial Natural Science Foundation,China(LGN18C140007 and Y20C140024)the National High Technology Research and Development Program of China(863 Program,2013AA102402)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
基金Supported by the Ministerial Level Advanced Research Foundation(51318020309)
文摘The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of wear zone of each end mill were analyzed, and three tool wear criterions of small-diameter end mills were defined. With the uEye camera, macro lens and 3-axis micro milling machine, it was proved the feasibility of measuring flank wear with the milling tests on a 45# steel workpiece. The design of experiment (DOE) showed that Vc was the most remarkable effect factor for the flank wear of small-diameter end mill. The wear curve of the experiments of milling was very similar to the Taylor curve.
文摘This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span><span><span><span> color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R</span><sup><span>2</span></sup><span> = 0.97), followed by the b* (R</span><sup><span>2</span></sup><span> = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.
文摘The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image are taken as research objects. On the base of the traditional checking methods of printing quality,combining the method and theory of digital image processing with printing theory in the new domain of image quality checking,it constitute the checking system of printing quality by image processing,and expound the theory design and the model of this system. This is an application of machine vision. It uses the high resolution industrial CCD(Charge Coupled Device) colorful camera. It can display the real-time photographs on the monitor,and input the video signal to the image gathering card,and then the image data transmits through the computer PCI bus to the memory. At the same time,the system carries on processing and data analysis. This method is proved by experiments. The experiments are mainly about the data conversion of image and ink limit show of printing.
基金funded by the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310).
文摘Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.
文摘Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.
基金supported by the State Administration of Forestry and Grass of the 948 Project of China(Grant No.2015-4-52)the support of the Fundamental Research Funds for the Central Universities(Grant No.2572017DB05)the support of the Natural Science Foundation of Heilongjiang Province(Grant No.C2017005)
文摘To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.
基金supported by National Key R&D Program of China( No. 2017YFB1303600)
文摘Tundish-covering flux bags can be depalletized and moved in the steel casting region using industrial robots and monocular vision simultaneously.An industrial robot mounted with a flexible vacuum sucker was used as the executor.For a structured bag model,a visual scheme based on the support vector machine and the histogram of oriented gradients was adopted.The computer was trained using a number of sample bag images that relied on the feature recognition algorithm.Finally,the automatic stacking and moving of the flux bags were realized.
文摘This paper describes industrial sorting system, which is based on robot vision technology, introduces main image processing methodology used during development, and simulates algorithm with Matlab. Besides, we set up image processing algorithm library via C# program and realize recognition and location for regular geometry workpiece. Furthermore, we analyze camera model in vision algorithm library, calibrate the camera, process the image series, and resolve the identify problem for regular geometry workpiece with different colours.
基金Supported by National Natural Science Foundation of China(Grant No51305031)
文摘Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.
基金supported by the National Natural Science Foundation of China (31471411)the Shanghai Agriculture Applied Technology Development Program,China ((2017)3-8-4)
文摘In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation(PAR)(A-TEP), aiming to explore the relationship between muskmelon(Cucumis melo L.) fruit attributes and environmental factors. Muskmelon surface color was described by parameters of red, green, blue, hue, saturation and brightness(HSI). Three characteristic parameters, gray level co-occurrence matrix(GLCM), angular second moment(ASM), entropy, contrast, and the coverage rate were used to describe the process of muskmelon fruit netting formation. ASM was not significant difference during muskmelon fruit growth. The number and deep of netting stripes gradually increased with fruit growth. Coverage rate increased rapidly for 15–30 d after pollination. The vertical and horizontal diameters of muskmelon fruit were followed a logistic curve. And root mean squared errors(RMSE) between the simulated and measured vertical and horizontal diameters were 3.527 and 4.696 mm, respectively. RMSE of red, green, blue, saturation and brightness were 0.999, 2.690, 2.992, 0.033 and 5.51, respectively, and the RMSE for entropy, contrast and coverage rates were 0.077, 0.063 and 0.015, respectively, indicating a well consistent between measured and simulated values.
基金Project supported by the National Natural Science Foundation ofChina (No. 60008001) and the Natural Science Foundation of Zhe-jiang Province (No. 300297), China
文摘A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402,Shanyou 10, Zhongyou207, Jiayou and Ilyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.
基金supported by the Beijing Municipal Science and Technology Project,China (Z151100001015004)
文摘This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds.Past research has shown that seed vigor is significantly related to the seed color and size,thus several physical features were identified as candidate predictors of high seed quality.Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101.In addition,binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination.Single-kernel germination tests were conducted to validate the predictive value of the identified features.The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds.Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features:three color features(R,a*,brightness),width,length,projected area,and single-kernel density,and weight.In contrast,fresh weight significantly negatively correlated with the feature of hue.In analyses of two of the highest correlating single features,germination percentage increased from 59.3 to 71.8% when a*≥3,and selection rate peaked at 57.8%.Germination percentage increased from 59.3 to 79.4%,and the selection rate reached 76.8%,when single-kernel weight≥0.0064 g.The most effective model was based on a multilayer perceptron(MLP)neural network,consisting of 15 physical traits as variables,and a stability calculated as 99.4%.Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%.These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection.Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.
基金This research was done as part of TEKES-funded PanFlow project and as part of a project OPTIMI funded by the Academy of Finland (grant number 117587) in Micro- and Nanosystems Research Group, Tampere University of Technology, Finland.
文摘When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB? environment. Im- ages are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.
文摘In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.
文摘To improve the identification for visual defect of TFT-LCD,a new machine vision system is proposed,which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing,such as average filtering and geometric correction,was performed on the captured image,and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast,objective and accurate inspection compared with subjective and inaccurate human eye inspection.
文摘With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable "defecs" that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.
文摘Automatic visual inspection of fabric is not only one of the potential application of machinevision but a considerable challenge in textile engineering as well.This paper mainly discusses howto inspect fabric defects using machine vision.The introduced inspection system has a feature of:(?)Categorizing the fabric defects into 4 groups,for each group diffcrent image processing and recog-nizing methods are designed for fast and efficient inspection:2.The inspection and recognitionparameters are determined by training and self learning,these parameters vary with different kindsof fabric;3.Human inspetor’s experiences are summed up as rules to ensure the system has a s(?)lar evaluation performance of human inspector.This system can detect most of the fab(?) defects.the total recognition error is less than 5% except for the detection error of yarn irregularity,whichcould be as high as 20%.
