Lightweight of Intelligent Real-Time Detection Model Based on YOLO-v4

With the gradual maturation of smart power grids and modern image processing technology, intelligent detection system based on substation is widely used. For the real-time requirements of substation intelligent inspection, this paper proposes the model of intelligent inspection of substation with light weight target detection. Firstly, the MobileNet network is introduced to build the lightweight backbone feature extraction network. Secondly, the module of multi-scale feature fusion is constructed to obtain more feature information. Finally, the scaling factor pruning model channel is introduced to build the model for lightweight target recognition. The proposed algorithm is compared with an existing algorithm in the simulation of the three datasets of Safety helmet (hardhat) wearing detect dataset, Hard Hat Workers dataset, Safety Helmet Detection dataset. Extensive experiments show that the proposed algorithm is more effective. The average recognition accuracy of the proposed algorithm is 0.932, and up to 38 frames per second are processed.