Deep Learning-based Personnel Dress Code And Behavior Detection Method In Pollution Remediation Environment

In the pollution remediation operation,it is of great significance for the work of pollution remediation to effectively guarantee the personal safety of workers in toxic environment.Based on the deep learning technology,this paper explores the personnel dress code and behavior detection method,and detects whether the personnel dress code and behavior are abnormal in real time,so as to improve the safety protection level of the staff in the pollution remediation environment.The main work content is as follows:(1)This subject analysis and comparison of traditional target detection algorithm and target detection algorithm based on depth study,the experimental results show that the traditional types of target detection algorithm is relatively single and greatly influenced by environmental factors,so the target detection algorithm based on deep learning is more suitable for complex pollution in this paper,we study the repair work environment.In this paper,a deep learning method is adopted to study the personnel dress code and behavior detection in pollution remediation environment.(2)Based on improved Faster R-CNN algorithm,the method of detecting personnel dress code was studied.Made the personnel environment pollution repair operations Dress Code dataset,contrast research Faster R-CNN algorithm commonly used backbone network and the division of the data sets in different ratio,optimal model,combining with the super parameter adjustment on the basis of regression tasks by optimizing algorithm of the objective function,experiments show that the final m AP value 1.06% increased and single image detection speed of 0.02 s.(3)Based on YOLOv3 algorithm,the method of personnel behavior detection is studied.Bahavior Dataset was made based on the status level of human behavior.The commonly used backbone network in YOLOv3 algorithm is used to divide different proportions of data sets,and the optimal model is obtained by combining with the adjustment of super parameters.The experiment shows that the m AP value can reach95.40%.(4)A real-time monitoring device for the toxic gas content in the pollution remediation environment is designed.Aiming at the problem that the cause of behavior cannot be determined when the behavior detection may be missed or wrong and the severity is "medium",the results of the toxic gas monitoring module arecomprehensively evaluated to improve the detection efficiency.Based on the deep learning technology,this paper studied the methods of personnel dress code detection and behavior detection,which effectively improved the safety protection level of workers in the environment of pollution remediation.