Aiming at the problem of low accuracy of defect detection of coal mine power equipment, this paper proposes a method for defect detection of coal mine power equipment based on an improved YOLOv5s. The method mainly includes three primary modifications. Firstly, a multibranched coordinate attention module is proposed, enhancing the ability of the model to obtain information about defect areas. Secondly, a feature fusion network module is proposed, which further enhances the feature expression and fusion ability of the model by connecting the non-adjacent feature information between the backbone network and the neck network across layers. Finally, a fast spatial pyramid pooling average pooling module is proposed, and the path of the neck network is embedded between the fusion networks to improve the ability of the shallow positioning information of the network to be transmitted to the deep layer. Experimental results demonstrate that the mAP@0.5 of improved YOLOv5s model in increased of 3.1%, and the F1 score is increased by 3%, meeting the detection demands of coal mine power equipment defects and has higher detection accuracy.