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A theoretical framework for improved fire suppression by linking management models with smart early fire detection and suppression technologies
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作者 Li Meng Jim O’Hehir +2 位作者 Jing Gao Stefan Peters Anthony Hay 《Journal of Forestry Research》 SCIE EI CAS CSCD 2024年第5期1-13,共13页
Bushfires are devastating to forest managers,owners,residents,and the natural environment.Recent tech-nological advances indicate a potential for faster response times in terms of detecting and suppressing fires.Howev... Bushfires are devastating to forest managers,owners,residents,and the natural environment.Recent tech-nological advances indicate a potential for faster response times in terms of detecting and suppressing fires.However,to date,all these technologies have been applied in isola-tion.This paper introduces the latest fire detection and sup-pression technologies from ground to space.An operations research method was used to assemble these technologies into a theoretical framework for fire detection and suppres-sion.The framework harnesses the advantages of satellite-based,drone,sensor,and human reporting technologies as well as image processing and artificial intelligence machine learning.The study concludes that,if a system is designed to maximise the use of available technologies and carefully adopts them through complementary arrangements,a fire detection and resource suppression system can achieve the ultimate aim:to reduce the risk of fire hazards and the dam-age they may cause. 展开更多
关键词 Forest fire Resource suppression Smart fire detection and suppression system Forest fire management Holistic system
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Enhancing Fire Detection Performance Based on Fine-Tuned YOLOv10
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作者 Trong Thua Huynh Hoang Thanh Nguyen Du Thang Phu 《Computers, Materials & Continua》 SCIE EI 2024年第11期2281-2298,共18页
In recent years,early detection and warning of fires have posed a significant challenge to environmental protection and human safety.Deep learning models such as Faster R-CNN(Faster Region based Convolutional Neural N... In recent years,early detection and warning of fires have posed a significant challenge to environmental protection and human safety.Deep learning models such as Faster R-CNN(Faster Region based Convolutional Neural Network),YOLO(You Only Look Once),and their variants have demonstrated superiority in quickly detecting objects from images and videos,creating new opportunities to enhance automatic and efficient fire detection.The YOLO model,especially newer versions like YOLOv10,stands out for its fast processing capability,making it suitable for low-latency applications.However,when applied to real-world datasets,the accuracy of fire prediction is still not high.This study improves the accuracy of YOLOv10 for real-time applications through model fine-tuning techniques and data augmentation.The core work of the research involves creating a diverse fire image dataset specifically suited for fire detection applications in buildings and factories,freezing the initial layers of the model to retain general features learned from the dataset by applying the Squeeze and Excitation attention mechanism and employing the Stochastic Gradient Descent(SGD)with a momentum optimization algorithm to enhance accuracy while ensuring real-time fire detection.Experimental results demonstrate the effectiveness of the proposed fire prediction approach,where the YOLOv10 small model exhibits the best balance compared to other YOLO family models such as nano,medium,and balanced.Additionally,the study provides an experimental evaluation to highlight the effectiveness of model fine-tuning compared to the YOLOv10 baseline,YOLOv8 and Faster R-CNN based on two criteria:accuracy and prediction time. 展开更多
关键词 fire detection ACCURACY prediction time fine-tuning real-time YOLOv10 Faster R-CNN
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Fire Detection Model Based on Improved RT-DETR
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作者 WU Xiao-ning SI Zhan-jun 《印刷与数字媒体技术研究》 CAS 北大核心 2024年第4期107-114,共8页
Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling m... Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling module was selected to improve the original convolution module,which improved the detection accuracy and reduced the number of parameter values.Using LSKA attention module on the backbone network further improved the detection accuracy.The experimental results showed that compared with the original RT-DETR model,the precision and mAP of FD-DETR flame detection are increased by 0.8%and 0.1%,respectively,which proves that the improved method proposed in this study effectively improves the feature extraction and feature fusion capabilities of the network.In the complex scene fire detection task,the performance of the improved RT-DETR algorithm is better than the original RT-DETR algorithm. 展开更多
关键词 fire detection RT-DETR Attention mechanism
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Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection
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作者 Ankan Kar Nirjhar Nath +1 位作者 Utpalraj Kemprai   Aman 《International Journal of Communications, Network and System Sciences》 2024年第2期11-29,共19页
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
关键词 Support Vector Machine Challenging Datasets Forest fire detection CLASSIFICATION
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Convolutional Neural Network Model for Fire Detection in Real-Time Environment 被引量:1
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作者 Abdul Rehman Dongsun Kim Anand Paul 《Computers, Materials & Continua》 SCIE EI 2023年第11期2289-2307,共19页
Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing... Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly. 展开更多
关键词 fire detection industrial surveillance system smart devices smart social agent(SSA) machine learning algorithms CNN
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Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment 被引量:1
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作者 Naqqash Dilshad Taimoor Khan JaeSeung Song 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期749-764,共16页
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
关键词 Deep learning DRONE embedded vision emergency monitoring fire classification fire detection IOT search and rescue
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Fusion-Based Deep Learning Model for Automated Forest Fire Detection
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作者 Mesfer Al Duhayyim Majdy M.Eltahir +5 位作者 Ola Abdelgney Omer Ali Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah 《Computers, Materials & Continua》 SCIE EI 2023年第10期1355-1371,共17页
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
关键词 Environment monitoring remote sensing forest fire detection deep learning machine learning fusion model
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A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment 被引量:8
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作者 Teng Wang Leping Bu +2 位作者 Zhikai Yang Peng Yuan Jineng Ouyang 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2020年第1期263-275,共13页
In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space... In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment. 展开更多
关键词 Color dispersion centroid motion expert system RGB-HIS color model SIMILARITY video fire detection
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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM 被引量:4
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作者 Fangming Bi Xuanyi Fu +3 位作者 Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa 《Computers, Materials & Continua》 SCIE EI 2020年第1期199-216,共18页
Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed... Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios. 展开更多
关键词 fire detection image segmentation feature extraction fruit fly optimization support vector machine
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Sequential Pattern Technology for Visual Fire Detection 被引量:2
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作者 Yu-Chiang Li Wei-Cheng Wu 《Journal of Electronic Science and Technology》 CAS 2012年第3期276-280,共5页
Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color mo... Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik's method. 展开更多
关键词 Sequential pattern statistical colormodel visual fire detection.
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Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks 被引量:1
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作者 Yongsheng Liu Yansong Yang +1 位作者 Chang Liu Yu Gu 《ZTE Communications》 2015年第2期12-16,共5页
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
关键词 forest fire detection artificial neural network wireless sensor network
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An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System
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作者 Arwa A.Mashat Niayesh Gharaei Aliaa M.Alabdali 《Computers, Materials & Continua》 SCIE EI 2022年第7期441-459,共19页
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
关键词 Forest fire detection rechargeable wireless sensor networks wireless mobile charger power constraint sustainable network lifetime
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A Review on Mine Fire Disasters and Assessment of Fire Detection Using a Dual-Cab Suppression System
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作者 Idongesit Bassey Utip Yulong Zhang +2 位作者 Li Ren Appiah Augustine Junfeng Wang 《Journal of Geoscience and Environment Protection》 2022年第12期29-44,共16页
The health and productivity of mining operations are negatively impacted by coal mine fires, making them dangerous. It happened everywhere, in both working and abandoned coal mines. This study seeks to review and prov... The health and productivity of mining operations are negatively impacted by coal mine fires, making them dangerous. It happened everywhere, in both working and abandoned coal mines. This study seeks to review and provide technical analytics of potential mine fires and fire detection in a Dual-Cab suppression system. Analysis was done on potential mine fires like spontaneous combustion, flammable gas explosions, and cab vehicle fires. Additionally, a review of the NIOSH experiment was conducted to assess the performance of smoke and flame detectors in a dual-cab suppression system. This study guides both open-pit and underground mining operations. Additionally, a few ideas and suggestions are presented to assist with on-the-job safety analysis, ensuing creative alterations, and technology advancement for the mining industry’s overall safety. 展开更多
关键词 Coal Spontaneous Combustion Mine fire fire detection Suppression System Dual-Cab
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One Fire Detection Method Using Neural Networks 被引量:13
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作者 程彩霞 孙富春 周心权 《Tsinghua Science and Technology》 SCIE EI CAS 2011年第1期31-35,共5页
A neural network fire detection method was developed using detection information for temperature smoke density, and CO concentration to determine the probability of three representative fire conditions. The method ove... A neural network fire detection method was developed using detection information for temperature smoke density, and CO concentration to determine the probability of three representative fire conditions. The method overcomes the shortcomings of domestic fire alarm systems using single sensor information. Test results show that the identification error rates for fires, smoldering fires, and no fire are less than 5%, which greatly reduces leak-check rates and false alarms. This neural network fire alarm system can fuse a variety of sensor data and improve the ability of systems to adapt in the environment and accurately predict fires, which has great significance for life and property safety. 展开更多
关键词 fire detection neural network multi-sensor information fusion SIMULATION
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Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3 被引量:3
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作者 Yue-Yan Qin Jiang-Tao Cao Xiao-Fei Ji 《International Journal of Automation and computing》 EI CSCD 2021年第2期300-310,共11页
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee... Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate. 展开更多
关键词 fire detection depthwise separable convolution fire classification You Only Look Once version 3(YOLOv3) target regression
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Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data 被引量:2
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作者 Bryan Hally Luke Wallace +2 位作者 Karin Reinke Simon Jones Andrew Skidmore 《International Journal of Digital Earth》 SCIE EI 2019年第9期1030-1045,共16页
A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostation... A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostationary sensors increase the data available to describebackground temperature in the temporal domain. Broad area methodsto extract the expected diurnal cycle of a pixel using this temporally richdata have shown potential for use in fire detection. This paper describesan application of a method for priming diurnal temperature fitting ofimagery from the Advanced Himawari Imager. The BAT method is usedto provide training data for temperature fitting of target pixels, to whichthresholds are applied to detect thermal anomalies in 4 μm imageryover part of Australia. Results show the method detects positive thermalanomalies with respect to the diurnal model in up to 99% of caseswhere fires are also detected by Low Earth Orbiting (LEO) satellite activefire products. In absence of LEO active fire detection, but where aburned area product recorded fire-induced change, this method alsodetected anomalous activity in up to 75% of cases. Potentialimprovements in detection time of up to 6 h over LEO products are alsodemonstrated. 展开更多
关键词 fire detection diurnal variation geostationary sensors broad area training advanced himawari imager
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Using knowledge inference to suppress the lamp disturbance for fire detection 被引量:3
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作者 Kailai Sun Qianchuan Zhao Xinwei Wang 《Journal of Safety Science and Resilience》 CSCD 2021年第3期124-130,共7页
Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smo... Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and fire.Recently,research on convolutional neural networks(CNN)for fire image detection has become a hot topic.However,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm rate.Besides,light and lamps often exist in buildings.To address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp disturbance.Firstly,we train the YoloV3 network to detect and recognize lamps.Secondly,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation method.Last,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 networks.The experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at https://github.com/kailaisun/fire-detection-without-lamp. 展开更多
关键词 fire detection LAMP prior knowledge causal inference convolutional neural networks
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Smoldering charcoal detection in forest soil by multiple CO sensors
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作者 Chunmei Yang Yuning Hou +2 位作者 Tongbin Liu Yaqiang Ma Jiuqing Liu 《Journal of Forestry Research》 SCIE CAS CSCD 2023年第6期1791-1802,共12页
Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire.Existing residual fire detection equipment is mainly infrared... Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire.Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification.Due to the isolation of ground,temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious,making it difficult to identify by detection equipment.CO gas is an important detection index for indoor smoldering fire detection,and an important identification feature of hidden smoldering ground fires.However,there is no research on locating smoldering fires through CO detection.We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors.According to the motion decomposition search algorithm,the detection device realizes the function of automatically searching for smoldering charcoal.Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height.The size of the search step is related to the reliability of the search algorithm.The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure.The introduction of step and rotation factors in search algorithm improves the search efficiency.This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height.Based on this law,a CO gas sensor detection device for hidden smoldering fires has been designed,which enriches the technique of residual fire detection. 展开更多
关键词 Forest fi res Smoldering fire detection Wood carbon smoldering CO sensor
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MsFireD-Net:A lightweight and efficient convolutional neural network for flame and smoke segmentation
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作者 F.M.Anim Hossain Youmin Zhang 《Journal of Automation and Intelligence》 2023年第3期130-138,共9页
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
关键词 Forest fire detection Convolutional neural network Semantic segmentation UAV Efficient upsampling
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Satellite remote-sensing technologies used in forest fire management 被引量:7
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作者 TIANXiao-rui DouglasJ.Mcrae SHULi-fu WANGMing-yu LIHong 《Journal of Forestry Research》 SCIE CAS CSCD 2005年第1期73-78,i004,共7页
Satellite remote sensing has become a primary data source for fire danger rating prediction, fuel and fire mapping, fire monitoring, and fire ecology research. This paper summarizes the research achievements in these ... Satellite remote sensing has become a primary data source for fire danger rating prediction, fuel and fire mapping, fire monitoring, and fire ecology research. This paper summarizes the research achievements in these research fields, and discusses the future trend in the use of satellite remote-sensing techniques in wildfire management. Fuel-type maps from remote-sensing data can now be produced at spatial and temporal scales quite adequate for operational fire management applications. US National Oceanic and Atmospheric Administration (NOAA) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites are being used for fire detection worldwide due to their high temporal resolution and ability to detect fires in remote regions. Results can be quickly presented on many Websites providing a valuable service readily available to fire agency. As cost-effective tools, satellite remote-sensing techniques play an important role in fire mapping. Improved remote-sensing techniques have the potential to date older fire scars and provide estimates of burn severity. Satellite remote sensing is well suited to assessing the extent of biomass burning, a prerequisite for estimating emissions at regional and global scales, which are needed for better understanding the effects of fire on climate change. The types of satellites used in fire research are also discussed in the paper. Suggestions on what remote-sensing efforts should be completed in China to modernize fire management technology in this country are given. 展开更多
关键词 Satellite remote sensing fire management Fuel mapping fire detection REVIEW
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