Helmet Wearing Detection in Electric Power Field Based on Millimeter-wave Radar and Visual Fusion
The failure of power site operators to wear safety helmets is one of the important causes of safety accidents.In order to prevent the recurrence of similar accidents,using millimeter-wave radar and vision fusion technology,a set of intelligent recog-nition algorithms that can detect whether workers are wearing safety helmets in real time based on edge computing equipment has been developed.Firstly,the conversion relationship between the coordinate systems is calculated and the joint calibration is car-ried out to achieve spatial fusion,Secondly,synchronization of radar and visual data is realized by timestamp matching.Then,the millimeter-wave radar data is preprocessed and the image region of interest(ROI)is calculated;finally,based on the YOLO v5 model,the improved lightweight network ShuffleNetv2 is used as the backbone network and the loss function is replaced to im-prove the network operation speed,and the personnel wearing safety helmets are detected in the ROI.The experimental platform was built in the electric power field and the algorithm was compared with the existing pure vision scheme.The results showed that the proposed method was slightly improved compared with the existing advanced methods in terms of detection accuracy,and greatly improved compared with the pure vision scheme in terms of real-time performance,which could realize real-time detec-tion at the operation site.
万方数据
维普
