Railway catenary system is the core equipment to supply power to electric traction vehicles, and its state directly affects the safety of train operation. In order to solve the problem that the key small components (split pin, tube sleeve and nut of positioner bracket) in the railway catenary are difficult to accurately locate due to their small size and complex environment, a small target detection method based on improved YOLOv5s is proposed. Firstly, the C3 module of the feature extraction network is combined with the linear deformable convolution （LDConv） to design a new C3_LD module. The proposed module employs deformable convolution kernels to dynamically adjust receptive fields, which effectively captures geometric deformation characteristics of small targets. This design not only enhances feature extraction capability but also reduces parameter. Secondly, the SPPFCSPC_group structure is designed to replace the original SPPF structure, and the multi-scale feature expression ability of the network is improved by combining group convolution with multi-scale spatial pyramid. Finally, the original loss function is replaced by spatial intersection over union (SIoU), which enhances bounding box regression accuracy through spatial constraints between predicted and ground-truth boxes. The results of ablation experiments and comparison experiments show that the improved algorithm in this paper achieves 93.2% mean average precision (mAP) and 93.1% recall rate in the detection task of railway catenary mesh small components, which are 1.9% and 3.6% higher than those of the original algorithm, which effectively alleviates the missed detection and false detection problems of railway catenary small components.