Abstract: As a crucial component of a high-speed rail traction power supply system, the catenary system is responsible for transmitting electrical energy to electric multiple units (EMUs). In practice, continuous impacts from pantograph-net interactions and external environmental factors can lead to defects in the catenary’s supporting parts, such as looseness, detachment, fracture, and cracking. These issues compromise the reliability of the catenary structure and pose risks to its stable operation. Therefore, timely and accurate positioning of the catenary support components (CSCs) is vital for ensuring the safe operation of high-speed rails and improving the catenary maintenance strategies. In 2012, the former Ministry of Railways of China (now the China Railway Corporation) officially promulgated the General Technical Specifications for High-speed Railway Power Supply Safety Detection and Monitoring System. This study marked a shift from traditional manual inspection methods to intelligent non-contact catenary detection and maintenance using computer vision technology. This study addresses challenges in detection systems by focusing on “catenary part positioning” in the whole detection process from the perspective of the functional integrity of the detection system. Detecting CSCs is challenging because of the variety of parts, scale differences, and small size of components. To overcome these challenges, this study proposes a catenary component detection algorithm that utilizes a multiscale fusion pyramid focus network. This approach integrates a balance module and a feature pyramid module to improve the detection performance of small targets. The separable residual pyramid aggregation module (SRPAM) was designed to optimize multi-scale feature extraction, expand the receptive field, and address multi-scale issues in CSC detection. Furthermore, a path aggregation network based on the equilibrium feature pyramid (PA-BFPN) was designed to improve cross-layer feature fusion efficiency and small object detection performance. Finally, the effectiveness of the proposed method is demonstrated through comparative experiments, visual analysis of the results, multi-scale feature fusion module experiments, feature pyramid network experiments, and ablation studies. The results demonstrate that the proposed multiscale feature pyramid FCOS (MFP-FCOS) algorithm offers excellent overall performance compared to many classical algorithms. Visualization experiments confirm its effectiveness in detecting targets across different scales and effectively solving small-scale and multi-scale sample detection challenges. The proposed SPRAM effectively mitigates information loss and improves feature extraction performance, whereas the proposed PA-BFPN obtains more comprehensive feature information. In summary, the proposed MFP-FCOS achieved a detection accuracy (mAP) of 48.6% on the CSC dataset with 30 floating point operations per second (FLOPs), indicating a balanced trade-off between detection accuracy and detection speed.