Insulation Detection Algorithm for Gloves Based on Improved YOLOv8n

Abstract

With the rapid development of China's industry, the safety problems in the process of electrical operation are highly concerned by the electrical industry. In order to reduce the incidence of electrical safety accidents, it is very necessary to wear insulating gloves for electric power construction, which is also of great significance to ensure the safety of electric power production. However, the small target of insulating gloves and the complexity of the electrician operation site, which undoubtedly increases the difficulty of identifying the algorithm model of insulating gloves. For this problem, insulation glove target detection algorithm based on YOLOv8n improvement model. Through many experiments, YOLOv8n was selected as the basic framework of the insulating glove detection algorithm. In order to make the model more accurately locate and identify the targets of interest, the efficient coordinate attention mechanism Coordinate Attention was inserted into backbone to improve the accuracy of the model. The introduction of PConv in necbricks reduces redundant computation and memory access for a more efficient extraction of spatial features. After the improved YOLOv8n algorithm, the mAP is increased from the initial 96.4% to 98.6%, which provides a good reference value for the subsequent research of the target detection algorithm of insulating gloves.