This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to...This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.展开更多
Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and thei...Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.展开更多
A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （...A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.展开更多
Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes i...Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire detection.In the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor nodes.In WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes wirelessly.Likewise,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive devices.However,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor nodes.To solve this problem,multiple power banks can be employed instead of utilizing WMCs.Furthermore,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works.However,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy wirelessly.Consequently,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes.Therefore,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based systems.Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related schemes.In conclusion,the proposed scheme significantly improves network energy efficiency for WSN.展开更多
With the rising frequency and severity of wildfires across the globe,researchers have been actively searching for a reliable solution for early-stage forest fire detection.In recent years,Convolutional Neural Networks...With the rising frequency and severity of wildfires across the globe,researchers have been actively searching for a reliable solution for early-stage forest fire detection.In recent years,Convolutional Neural Networks(CNNs)have demonstrated outstanding performances in computer vision-based object detection tasks,including forest fire detection.Using CNNs to detect forest fires by segmenting both flame and smoke pixels not only can provide early and accurate detection but also additional information such as the size,spread,location,and movement of the fire.However,CNN-based segmentation networks are computationally demanding and can be difficult to incorporate onboard lightweight mobile platforms,such as an Uncrewed Aerial Vehicle(UAV).To address this issue,this paper has proposed a new efficient upsampling technique based on transposed convolution to make segmentation CNNs lighter.This proposed technique,named Reversed Depthwise Separable Transposed Convolution(RDSTC),achieved F1-scores of 0.78 for smoke and 0.74 for flame,outperforming U-Net networks with bilinear upsampling,transposed convolution,and CARAFE upsampling.Additionally,a Multi-signature Fire Detection Network(MsFireD-Net)has been proposed in this paper,having 93%fewer parameters and 94%fewer computations than the RDSTC U-Net.Despite being such a lightweight and efficient network,MsFireD-Net has demonstrated strong results against the other U-Net-based networks.展开更多
文摘This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/172/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.
文摘A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.
文摘Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire detection.In the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor nodes.In WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes wirelessly.Likewise,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive devices.However,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor nodes.To solve this problem,multiple power banks can be employed instead of utilizing WMCs.Furthermore,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works.However,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy wirelessly.Consequently,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes.Therefore,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based systems.Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related schemes.In conclusion,the proposed scheme significantly improves network energy efficiency for WSN.
文摘With the rising frequency and severity of wildfires across the globe,researchers have been actively searching for a reliable solution for early-stage forest fire detection.In recent years,Convolutional Neural Networks(CNNs)have demonstrated outstanding performances in computer vision-based object detection tasks,including forest fire detection.Using CNNs to detect forest fires by segmenting both flame and smoke pixels not only can provide early and accurate detection but also additional information such as the size,spread,location,and movement of the fire.However,CNN-based segmentation networks are computationally demanding and can be difficult to incorporate onboard lightweight mobile platforms,such as an Uncrewed Aerial Vehicle(UAV).To address this issue,this paper has proposed a new efficient upsampling technique based on transposed convolution to make segmentation CNNs lighter.This proposed technique,named Reversed Depthwise Separable Transposed Convolution(RDSTC),achieved F1-scores of 0.78 for smoke and 0.74 for flame,outperforming U-Net networks with bilinear upsampling,transposed convolution,and CARAFE upsampling.Additionally,a Multi-signature Fire Detection Network(MsFireD-Net)has been proposed in this paper,having 93%fewer parameters and 94%fewer computations than the RDSTC U-Net.Despite being such a lightweight and efficient network,MsFireD-Net has demonstrated strong results against the other U-Net-based networks.
