To prevent economic,social,and ecological damage,fire detection and management at an early stage are significant yet challenging.Although computationally complex networks have been developed,attention has been largely...To prevent economic,social,and ecological damage,fire detection and management at an early stage are significant yet challenging.Although computationally complex networks have been developed,attention has been largely focused on improving accuracy,rather than focusing on real-time fire detection.Hence,in this study,the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment.The proposed model architecture is inspired by the VGG16 network,with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4.This results in higher performance with a reduced number of parameters and inference time.Moreover,smaller convolutional kernels are utilized,which are particularly designed to obtain the optimal details from input images,with numerous channels to assist in feature discrimination.In E-FireNet,three steps are involved:preprocessing of collected data,detection of fires using the proposed technique,and,if there is a fire,alarms are generated and transmitted to law enforcement,healthcare,and management departments.Moreover,E-FireNet achieves 0.98 accuracy,1 precision,0.99 recall,and 0.99 F1-score.A comprehensive investigation of various Convolutional Neural Network(CNN)models is conducted using the newly created Fire Surveillance SV-Fire dataset.The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy,model size,and execution time.展开更多
A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure ...A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.展开更多
The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of con...The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.展开更多
基金This work was supported by the Institute for Information&Communications Technology Promotion(IITP)grant funded by the Korean government(MSIT)(No.2020-0-00959).
文摘To prevent economic,social,and ecological damage,fire detection and management at an early stage are significant yet challenging.Although computationally complex networks have been developed,attention has been largely focused on improving accuracy,rather than focusing on real-time fire detection.Hence,in this study,the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment.The proposed model architecture is inspired by the VGG16 network,with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4.This results in higher performance with a reduced number of parameters and inference time.Moreover,smaller convolutional kernels are utilized,which are particularly designed to obtain the optimal details from input images,with numerous channels to assist in feature discrimination.In E-FireNet,three steps are involved:preprocessing of collected data,detection of fires using the proposed technique,and,if there is a fire,alarms are generated and transmitted to law enforcement,healthcare,and management departments.Moreover,E-FireNet achieves 0.98 accuracy,1 precision,0.99 recall,and 0.99 F1-score.A comprehensive investigation of various Convolutional Neural Network(CNN)models is conducted using the newly created Fire Surveillance SV-Fire dataset.The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy,model size,and execution time.
基金This work was financially supported by MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-RS-2022-00156354)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program(Project No.P0016038).
文摘A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1I1A1A01055652).
文摘The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.
