Abstract: To address the challenges in detecting defects such as loosening, strand separation, and breakage in critical components like elastic suspension cables, suspension wires, and electrical connections within electrified railway catenary systems, this paper proposes a comprehensive scanning-based intelligent catenary inspection system. Mounted on contact network inspection vehicles, work vehicles, or other specialized rail vehicles, the system employs dual high-definition line-scan cameras for image acquisition. It utilizes an enhanced YOLOv11n algorithm to achieve precise localization of elastic suspension cables, suspension wires, and electrical connections within the contact network, with an accuracy rate of 96.52%. Furthermore, the lightweight FastestDet model is applied for defect identification across these three component types, achieving an accuracy rate of 96.8%. The modified YOLOv11n algorithm achieves a 2.14% accuracy improvement over standard YOLOv11n, while the FastestDet algorithm surpasses YOLO-Fastestv2 by 2.3%. During field testing, the system demonstrated over 90% defect recognition accuracy, effectively enabling “proactive early warning” for critical component conditions. This innovation provides a cutting-edge technical solution for intelligent operation and maintenance of electrified railway power supply systems.