Fusing PSA to Improve YOLOv5s Detection algorithm for Electric Power Operation Wearable devices

Abstract

In order to determine whether the electric power workers wear safety equipment such as safety helmet, insulation boots, insulation gloves, insulation clothes, etc., to ensure the safety of the electric power construction site. We propose a electric power operation safety equipment detection algorithm incorporating PSA to improve YOLOv5s algorithm, using polarized self-attention mechanism to improve the feature extraction end of YOLOv5s algorithm, improving the channel resolution and spatial resolution of safety equipment images of electric power operation scenes, and preserving the information of key nodes of small targets that are obscured; GSConv is used to replace the ordinary convolution to reduce the complexity of the model, improve the calculation speed of the algorithm and improve the detection accuracy. The experimental results show that the average accuracy mean (IoU = 0.5) of the proposed algorithm reaches 0.961, which is 1.58% higher than that of the original network detection performance, and the model parameters are reduced from 7.03 to 5.48 millions. It effectively improves the detection speed and accuracy of the algorithm, and can effectively monitor whether the operator wears the safety equipment correctly when there are occlusions and missing safety equipment in the electric power operation scene, which has a excellent application effect.