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
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
基金This work was supported by National Natural Science Foundation of China(61973094).
文摘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.
基金This work was supported by the Open Fund Project[grant number Mz2022KF05]of Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan Province,the National Science Foundation of China[Grant No.72204155]the Natural Science Foundation of Shanghai[grant number 23ZR1423100]。
文摘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.
