Research And Implementation Of Forest Fire Detection And Recognition System Based On Deep Learnin

Forest fires have caused great harm to the ecological resources of the earth and the survival and development of human beings.Traditional manual inspection,sensor detection and remote sensing detection technologies have been applied to forest fire detection,but there are problems such as low reliability and high cost,and it is difficult to realize real-time and accurate fire monitoring and early warning.Therefore,this paper aims to use computer vision technology to develop an efficient and accurate forest fire identification and detection method to improve the supervision of forest fires.The specific research work of this paper is as follows.In order to find the fire source in time,avoid the fire from expanding and eliminate possible fire hazards,this paper proposes two forest fire detection models suitable for high-altitude inspections,namely YOLO＿MC and YOLO＿MCLite.Among them,the YOLO＿MC model can effectively detect open flames and smoke in standard images,and the YOLO＿MCLite model based on this model for lightweight design is suitable for the detection of high-temperature areas in thermal images.It’s verified by experiments that the network model can effectively detect forest fires,find high temperature points in time and prevent the occurrence of fires.During high-altitude remote sensing shooting,the diffuse smoke will make it difficult to accurately detect the fire source,and complex backgrounds,low contrast,and lighting changes will also interfere with flame detection.To overcome these problems,this paper improves a recurrent adversarial generative network to improve forest fire detection accuracy.In the generator module,the lightweight Transformer module is combined with the convolution module to improve the perception of target details,and a multi-level discriminator is designed to improve the discrimination accuracy of the generated samples.In addition,the calculation of background loss and edge loss is added to optimize the cycle consistency loss function.Experimental results show that the designed recurrent adversarial generative network can generate better picture quality than other networks,and can detect the position and shape of flames more accurately.Finally,the design of forest fire detection system based on the proposed algorithm is introduced.In order to meet the actual needs,the system adopts B/S architecture and objectoriented software design method,and realizes the functions of real-time browsing of video images,fire recognition and fire scene information collection.Through the detailed system overall architecture and database structure design,the system can provide effective support and help for forest fire detection,so as to protect forest resources and human life and property safety.