关键词: 安全装备检测, 绝缘手套, YOLO, 融合Transformer, 损失函数

Abstract: Aiming at the low accuracy and poor generalization of the target detection algorithm for safety equipment such as safety helmets, insulating gloves, and insulating shoes of traditional substation electric personnel, especially for the difficulty of detecting whether to wear insulating gloves or not, an improved YOLOv8 detection algorithm Wear-YOLO for substation power personnel safety equipment is proposed. In order to better learn the contextual information of complex scenes, the C2f （CSP bottleneck with 2 convolutions） module of the Backbone part of YOLOv8 is replaced with the MobileViTv3 module that integrates the Transformer structure to capture long-distance dependencies and contextual information and combine it with local information. And the feature extraction capability of the model is improved in substation scenarios. At the same time, in order to optimize the small target detection effect, a small target detection layer is introduced to enhance the  extraction of the network in shallow semantic information, thereby improving the  detection accuracy for small targets not wearing insulating gloves. WIoUv3 is used to improve the bounding box regression loss function, and a dynamic non-monotonic focusing mechanism is introduced to make the model focuses more on ordinary quality anchor boxes, thus improving the accuracy of model detection and its adaptability to complex situations. The experimental results show that the average detection accuracy is 92.1%, the detection accuracy of helmets is 96.8%, the detection accuracy of wearing insulating gloves is 92.1%, the detection accuracy of not wearing insulating gloves is 81.6%, and the detection accuracy of insulating shoes is 93.0%. Compared with the classic target detection models Faster R-CNN, SSD, RetinaNet, and YOLOv5, it has better detection accuracy and robustness.

Key words: safety equipment detection, insulating gloves, YOLO, fusion Transformer, loss function