Research On Smoke Detection Technology And Application Based On Deep Learning

With the continuous development of computer vision and image processing technology,fire detection methods based on deep learning have become a hot topic of current research.These new fire detection methods can not only adapt to complex and vast outdoor environments,but also significantly improve the accuracy and effectiveness of fire smoke detection.However,most deep learning algorithms still need to be improved in terms of real-time performance and detection accuracy in special scenarios.This thesis conducts in-depth research on smoke detection algorithms based on deep learning,and the main contents include:First of all,to ensure the accuracy of detection and meet the requirement of real-time performance,this paper proposes a lightweight smoke detection algorithm based on multi-feature hybrid deep learning.The algorithm first uses Mobile Netv3 with fewer parameters to replace the backbone network of YOLOv5 s,and adds SE attention mechanism to enhance the semantic information of each channel after convolution;then introduces CSP＿ACON module to improve the performance and accuracy of feature extraction;finally,to deal with the interference of sky clouds,a method of image pre-screening based on smoke multi-features is added.Experimental results show that the algorithm proposed in this paper improves the network detection speed by about 17%compared with the original YOLOv5 s,while ensuring accuracy,meeting the requirement of real-time detection.Secondly,to address the problem of convolutional neural networks having difficulty in extracting effective features in small smoke detection,this paper proposes an improved YOLOv7 smoke detection algorithm.The algorithm first uses multi-scale feature fusion to reduce the downsampling ratio,reduce the feature receptive field,and enhance the detection ability of small smoke;then borrows the idea of BIFPN to optimize the neck layer of YOLOv7,enhance the feature information transmission between different network layers,and improve the network accuracy;finally,adds CBAM-n SAM module to each branch of head,making the network reduce the attention to irrelevant information,focus on analyzing key features,and further improve the network detection accuracy.Experimental results show that the improved YOLOv7 algorithm effectively improves the detection accuracy in small smoke scenarios.Last but not least,combining the methods proposed above,this paper designs and implements a smoke detection system that can monitor forest fires.First,the requirements of forest fire monitoring system are analyzed.Second,the overall framework of smoke detection system is designed and the functions of each module are analyzed.Finally,the specific implementation of the system in edge devices is introduced.The system proposed in this paper can process fire smoke information in real time and efficiently and send out early warning signals in time,providing some reference value for forest fire prevention and control.