Foreign object detection system in catenary networks based on novel cascade convolutional neural networks

Railway catenary systems are essential equipment for electrified railways, and their safe and stable operation directly affects the regular movement of trains and the safety of passengers' lives and property. Foreign objects are one of the main threats to the safety of the catenary system, making the detection and classification of these foreign objects a challenging task. This paper proposes a method that cascades the YOLOv5-lite and DenseNet-121 networks, carrying out detection and classification in separate steps. This approach facilitates detection in high-resolution scenes with complex backgrounds, thereby reducing computational overhead and increasing accuracy. Such a method ensures that the detection network does not overly focus on the minute details of the foreign objects in the catenary system.