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Power plant personnel need to wear insulation gloves during troubleshooting. If they do not wear insulation gloves, serious electric shocks may occur. Aiming at the problem of low detection accuracy of insulation gloves worn by workers in power plant, a detection method of insulation gloves worn by workers in power plant based on improved YOLOv5s (You Only Look Once v5s) was proposed. Firstly, self-calibrating convolution is introduced in the detection algorithm to effectively enlarge the receptive field and strengthen the ability of the network to extract weak features. Secondly, the attention mechanism SK is added to make the network pay more attention to the target to be detected. Finally, the loss function of the original YOLOv5s is replaced by EIOU to further improve the detection accuracy of insulation gloves. The experimental results show that compared with the original YOLOv5s network, the improved network improves the detection accuracy of insulation gloves wearing, and the mAP (mean Average Precision) is increased by 2.4%, which proves the practicability and efficiency of the algorithm