The small defects in overhead catenary system (OCS) can result in long time delays, economic loss and even passenger injury. However, OCS images exhibit great variations with complex background and oblique views which pose a great challenge for small defects detection in high-speed rail system. In this paper, we propose the spatial-prior-guided attention for small object detection in OCS with two main advantages: (1) The spatial-prior is proposed to retain the spatial information between small defects and the electric components in OCS. (2) Based on spatial-prior, the spatial-prior-guided attention model (SAM) is designed to highlight useful information in the features and suppress redundant features response. SAM can model the spatial relations progressively and can be integrated with state-of-the-art feed-forward network architecture with end-to-end training fashion. We conduct extensive experiments on both Split pin datasets and PASCAL–VOC datasets and achieve 97.2% and 79.5% mAP values, respectively. All the experiments demonstrate the competitive performance of our method.