<abstract> <p>Improperly using safety harness hooks is a major factor of safety hazards during power maintenance operation. The machine vision-based traditional detection methods have low accuracy and limited real-time effectiveness. In order to quickly discern the status of hooks and reduce safety incidents in the complicated operation environments, three improvements are incorporated in YOLOv5s to construct the novel HDS-YOLOv5 network. First, HOOK-SPPF (spatial pyramid pooling fast) feature extraction module replaces the SPPF backbone network. It can enhance the network's feature extraction capability with less feature loss and extract more distinctive hook features from complex backgrounds. Second, a decoupled head module modified with confidence and regression frames is implemented to reduce negative conflicts between classification and regression, resulting in increased recognition accuracy and accelerated convergence. Lastly, the Scylla intersection over union (SIoU) is employed to optimize the loss function by utilizing the vector angle between the real and predicted frames, thereby improving the model's convergence. Experimental results demonstrate that the HDS-YOLOv5 algorithm achieves a 3% increase in mAP@0.5, reaching 91.2%. Additionally, the algorithm achieves a detection rate of 24.0 FPS (frames per second), demonstrating its superior performance compared to other models.</p> </abstract>