检索结果-南通市图书馆

Efficient Deep Learning Framework for fire detection in Complex Surveillance Environment

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第7期46卷 749-764页

作者： Naqqash Dilshad Taimoor Khan JaeSeung Song Department of Convergence Engineering for Intelligent Drone Seoul05006Korea Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan

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.

关键词： Deep learning drone embedded vision emergency monitoring fire classification fire detection IoT search and rescue

fire detection Method Based on Depthwise Separable Convolution and YOLOv3

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Automation and computing 2021年第2期18卷 300-310页

作者： Yue-Yan Qin Jiang-Tao Cao Xiao-Fei Ji School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001China School of Automation Shenyang Aerospace UniversityShenyang 110136China

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.

关键词： fire detection depthwise separable convolution fire classification You Only Look Once version 3(YOLOv3) target regression

fire detection Method Based on Improved Fruit Fly Optimization-Based SVM

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2020年第1期62卷 199-216页

作者： Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa College of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Mine Digitization Engineering Research Center of the Ministry of EducationChina University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Micro-System and Information TechnologyChinese Academy of SciencesShanghai201899China Infromation Communication Technology Department Wollo UniversityDessie EthiopiaP.o Box 1145Ethiopia

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.

关键词： fire detection image segmentation feature extraction fruit fly optimization support vector machine

Using knowledge inference to suppress the lamp disturbance for fire detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Safety Science and Resilience 2021年第3期2卷 124-130页

作者： Kailai Sun Qianchuan Zhao Xinwei Wang Center for Intelligent and Networked Systems Department of AutomationBNRistTsinghua UniversityBeijingChina

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://***/kailaisun/fire-detection-without-lamp.

关键词： fire detection lamp prior knowledge causal inference convolutional neural networks

Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2019年第9期12卷 1030-1045页

作者： Bryan Hally Luke Wallace Karin Reinke Simon Jones Andrew Skidmore School of Science RMIT UniversityMelbourneAustralia Bushfire and Natural Hazards Cooperative Research Centre MelbourneAustralia Faculty for Geo-Information Science and Earth Observation(ITC) University of TwenteEnschedeNetherlands Department of Environmental Science Macquarie UniversitySydneyAustralia

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.

关键词： fire detection diurnal variation geostationary sensors broad area training advanced himawari imager

A Review on Mine fire Disasters and Assessment of fire detection Using a Dual-Cab Suppression System

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Geoscience and Environment Protection 2022年第12期10卷 29-44页

作者： Idongesit Bassey Utip Yulong Zhang Li Ren Appiah Augustine Junfeng Wang College of Safety and Emergency Management Engineering Taiyuan University of Technology Taiyuan China College of Mining Engineering Taiyuan University of Technology Taiyuan China

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.

关键词： Coal Spontaneous Combustion Mine fire fire detection Suppression System Dual-Cab

One fire detection Method Using Neural Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2011年第1期16卷 31-35页

作者：程彩霞孙富春周心权 State Key Laboratory of Coal Resources and Mine Safety China University of Mining and Technology(Beijing) State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and TechnologyTsinghua University

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.

关键词： fire detection neural network multi-sensor information fusion simulation

Early smoke and flame detection based on transformer

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Safety Science and Resilience 2023年第3期4卷 294-304页

作者： Xinzhi Wang Mengyue Li Mingke Gao Quanyi Liu Zhennan Li Luyao Kou School of Computer Engineering and Science Shanghai UniversityShanghai 200444China The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai 201808China College of Civil Aviation Safety Engineering Civil Aviation Flight University of ChinaGuanghan 618307China Department of Engineering Physics Tsinghua UniversityBeijing 100084China

fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart cities.Early fire detection is imperative to mitigate potential hazards and minimize associated losses.However,existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection accuracy.To address this issue,in this study a decoder-free fully transformer-based(DFFT)detector is used to achieve early smoke and flame detection,improving the detection performance for fires of different sizes.This method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map prediction.First,data augmentation is performed to enhance the generalizability of the model.Second,the detection-oriented transformer(DOT)backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales,which are then fed into an encoder-only single-layer dense prediction module.Finally,the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder(SAE).The prediction module then aligns the classification and regression features using a task-aligned encoder(TAE)to ensure the semantic interaction of the classification and regression predictions.Experimental results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes,particularly early small fires.The DFFT achieved mean average precision(mAP)values of 87.40%and 81.12%for the two datasets,outperforming other baseline models.It exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features.

关键词： Early fire Smoke and flame detection fire detection Vision transformer Public safety

Method of fuzzy neural network for fire detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Progress in Natural Science:Materials International 1999年第8期 64-70页

作者：姚伟祥吴龙标卢结成范维澄 China Hefei 230027 State Key Laboratory of fire Science University of Science and Technology of China

A fuzzy neural network for fire detection is presented according to the characteristics of fire signals, and a calculating model of fuzzy inference is proposed to process fire signals. Fuzzy operations are applied to analyze synthetically different kinds of fire signals. Then, a calculating model of learning algorithm is created for a set of membership functions and fuzzy rules. The fuzzy neural network, which combines the techniques of neural network and fuzzy logic, offers a signal processing method for fire detection with fuzzy and adaptive characteristics simultaneously.

关键词： fuzzy neural network fire detection fuzzy operation learning algorithm model.