Aiming at the problem that the detection efficiency of high-speed railway catenary insulators is not high in complex background, the sample dataset is first expanded on a large scale. On the basis of the original YOLOv5s algorithm, in order to effectively improve the representation power of the model and increase the ECA attention mechanism, a cross-channel method without dimensionality reduction is carried out to focus on the position information of insulators. The BiFPN feature pyramid network is used to enrich the semantic information by multi-scale feature fusion. Select the Meta-ACON adaptive control activation function, and strictly control the upper and lower limits of the function within the maximum range allowed by the function to prevent the model from running out of control. The original GIOU loss function is replaced with the EIOU loss function, and the anchor box is further divided from the perspective of gradient, so as to improve the convergence speed of the network. Finally, according to the experimental results, the improved detection algorithm of YOLOv5s can be used to locate and identify the insulator more accurately, and the accuracy rate reaches 99.4%. The proposed detection algorithm provides a more accurate and faster method for insulator positioning detection.