Wearing inspection safety equipment such as insulating gloves and safety helmets is an important guarantee for safe power operations. Given the low accuracy of the traditional insulating gloves and helmet-wearing detection algorithm and the problems of missed detection and false detection, this paper proposes an improved safety equipment wearing detection model named RepGFPN-YOLOv5 based on YOLOv5. This paper first uses the K-Means + + algorithm to analyze the data set for Anchor parameter size re-clustering to optimize the target anchor box size; secondly, it uses the neck network (Efficient Reparameterized Generalized Feature Pyramid Network, RepGFPN), which combines the efficient layer aggregation network ELAN and the re-parameterization mechanism), to reconstruct the YOLOv5 neck network to improve the feature fusion ability of the neck network; reintroduce the coordinate attention mechanism (Coordinate Attention, CA) to focus on small target feature information; finally, use WIoU_Loss as the loss function of the improved model to reduce prediction errors. Experimental results show that the RepGFPN-YOLOv5 model achieves an accuracy increase of 2.1% and an mAP value of 2.3% compared with the original YOLOv5 network, and detection speed of the improved model reaches 89FPS.The code: https://github.com/CVChenXC/RepGFPN-YOLOv5.git.