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Fire Detection Algorithm Based on an Improved Strategy of YOLOv5 and Flame Threshold Segmentation
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作者 Zhao Yuchen Wu Shulei +3 位作者 Wang Yaoru Chen Huandong Zhang Xianyao Zhao Hongwei 《Computers, Materials & Continua》 SCIE EI 2023年第6期5639-5657,共19页
Due to the rapid growth and spread of fire,it poses a major threat to human life and property.Timely use of fire detection technology can reduce disaster losses.The traditional threshold segmentation method is unstabl... Due to the rapid growth and spread of fire,it poses a major threat to human life and property.Timely use of fire detection technology can reduce disaster losses.The traditional threshold segmentation method is unstable,and the flame recognition methods of deep learning require a large amount of labeled data for training.In order to solve these problems,this paper proposes a new method combining You Only Look Once version 5(YOLOv5)network model and improved flame segmentation algorithm.On the basis of the traditional color space threshold segmentation method,the original segmentation threshold is replaced by the proportion threshold,and the characteristic information of the flame is maximally retained.In the YOLOv5 network model,the training module is set by combining the ideas of Bootstrapping and cross validation,and the data distribution of YOLOv5 network training is adjusted.At the same time,the feature information after segmentation is added to the data set.Different from the training method that uses large-scale data sets for model training,the proposed method trains the model on the basis of a small data set,and achieves better model detection results,and the detection accuracy of the model in the validation set reaches 0.96.Experimental results show that the proposed method can detect flame features with faster speed and higher accuracy compared with the original method. 展开更多
关键词 YOLOv5 fire safety deep learning flame detection
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Boiler flame detection algorithm based on PSO-RBF network
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作者 吴进 GAO Yaqiong +1 位作者 YANG Ling ZHAO Bo 《High Technology Letters》 EI CAS 2023年第1期68-77,共10页
As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot ... As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence.In order to further improve the timeliness and accuracy of the flame detection network,a radial basis function(RBF)flame detection network based on particle swarm optimization(PSO)algorithm is proposed.First,the proposed algorithm initializes the speed and position parameters of the particles.Then,the parameters of the particles are mapped to the RBF flame detection network.Finally,the algorithm is iteratively updated to obtain the global optimal solution.The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation(BP)flame detection algorithm,and further improves the collection efficiency.The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples.In the flame data samples,the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%. 展开更多
关键词 radial basis function(RBF) particle swarm optimization(PSO) flame detection
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Improved High Speed Flame Detection Method Based on YOLOv7 被引量:6
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作者 Hongwen Du Wenzhong Zhu +1 位作者 Ke Peng Weifu Li 《Open Journal of Applied Sciences》 CAS 2022年第12期2004-2018,共15页
In order to solve the problems of the traditional flame detection method, such as low detection accuracy, slow detection speed and lack of real-time detection ability. An improved high speed flame detection method bas... In order to solve the problems of the traditional flame detection method, such as low detection accuracy, slow detection speed and lack of real-time detection ability. An improved high speed flame detection method based on YOLOv7 is proposed. Based on YOLOv7 and combined with ConvNeXtBlock, CN-B network module was constructed, and YOLOv7-CN-B flame detection method was proposed. Compared with the YOLOv7 method, this flame detection method is lighter and has stronger flame feature extraction ability. 2059 open flame data sets labeled with single flame categories were used to avoid the enhancement effect brought by high-quality data sets, so that the comparative experimental effect completely depended on the performance of the flame detection method itself. The results show that the accuracy of YOLOv7-CN-B method is improved by 5% and mAP is improved by 2.1% compared with YOLOv7 method. The detection speed reached 149.25 FPS, and the single detection speed reached 11.9 ms. The experimental results show that the YOLOv7-CN-B method has better performance than the mainstream algorithm. 展开更多
关键词 Light Weight Detection of flame YOLOv7-CN-B YOLOv7 ConvNeXt
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Design and implementation of gasifier flame detection system based on SCNN
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作者 WU Jin DAI Wei +1 位作者 WANG Yu ZHAO Bo 《High Technology Letters》 EI CAS 2022年第4期401-410,共10页
Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive ... Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive detection results of the current flame detection methods of gasifier and industrial boiler.A furnace flame detection model based on support vector machine convolutional neural network(SCNN)is proposed.This algorithm uses the advantages of neural networks in the field of image classification to process flame burning video sequences which needs detailed analysis.Firstly,the support vector machine(SVM)with better small sample classification effect is used to replace the Softmax classification layer of the convolutional neural network(CNN)network.Secondly,a Dropout layer is introduced to improve the generalization ability of the network.Subsequently,the area,frequency and other important parameters of the flame image are analyzed and processed.Eventually,the experimental results show that the flame detection model designed in this paper is more accurate than the CNN model,and the accuracy of the judgment on the flame data set collected in the gasifier furnace reaches 99.53%.After several ignition tests,the furnace flame of the gasifier can be detected in real time. 展开更多
关键词 support vector machine convolutional neural network(SCNN) support vector machine(SVM) flame detection flame image processing GASIFIER
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Gas pipeline explosion resistance technology 被引量:1
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作者 Zhao ZHU Zhen-Yuan JIA Kui-Jun WANG 《Journal of Coal Science & Engineering(China)》 2013年第3期353-357,共5页
Based on the features and requirements of gas drainage system, an optimized explosion resistance technology is done after a comprehensive analysis and research about the triple IR (Infrared Ray) flame detection tech... Based on the features and requirements of gas drainage system, an optimized explosion resistance technology is done after a comprehensive analysis and research about the triple IR (Infrared Ray) flame detection technology, explosion resistance valve technology and explosion resistance control technology. An intelligent PLC (Programmable Logic Controller) resistance control system is designed which can cut offthe gas branch quickly and accurately, and the controller have automatic pressure maintaining function, valve rotation limit function, remote and local control interlock function. The reliability and rationality of explosion resistance technology is verified by gas pipeline explosion propagation and resistance simulation test. Overall response time of explosion resistance system is less than 100ms, and the spread of fire in gas pipeline can be prevented effectively. 展开更多
关键词 explosion resistance technology flame detection fast shut-off valve intelligent control
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Early smoke and flame detection based on transformer 被引量:2
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作者 Xinzhi Wang Mengyue Li +3 位作者 Mingke Gao Quanyi Liu Zhennan Li Luyao Kou 《Journal of Safety Science and Resilience》 EI CSCD 2023年第3期294-304,共11页
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 losse... 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. 展开更多
关键词 Early fire Smoke and flame detection Fire detection Vision transformer Public safety
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Flame smoke detection algorithm based on YOLOv5 in petrochemical plant 被引量:1
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作者 Yueting Yang Shaolin Hu +1 位作者 Ye Ke Runguan Zhou 《International Journal of Intelligent Computing and Cybernetics》 EI 2023年第3期502-519,共18页
Purpose–Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety.The purpose of this paper is to solve the problem of missed detection and false detection in flame smo... Purpose–Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety.The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approach–This paper presents a flame smoke detection algorithm based on YOLOv5.The target regression loss function(CIoU)is used to improve the missed detection and false detection in target detection and improve the model detection performance.The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm.Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.Findings–Based on the actual situation of flame smoke,the loss function and activation function of YOLOv5 model are improved.Based on the improved YOLOv5 model,a flame smoke detection algorithm with generalization performance is established.The improved model is compared with SSD and YOLOv4-tiny.The accuracy of the improved YOLOv5 model can reach 99.5%,which achieves a more accurate detection effect on flame smoke.The improved network model is superior to the existing methods in running time and accuracy.Originality/value–Aiming at the actual particularity of flame smoke detection,an improved flame smoke detection network model based on YOLOv5 is established.The purpose of optimizing the model is achieved by improving the loss function,and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network.This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition. 展开更多
关键词 flame smoke detection Target recognition YOLOv5 Image detection Deep learning
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