Bird Nest Detection in Railway Catenaries Using a Coarse-to-Fine Strategy

Catenaries installed above railway lines are frequently exposed to the outdoor environment, making them prone to accumulating foreign objects, such as bird nests. These represent a primary risk to operational safety. Currently, the main method of catenary bird nest detection involves the manual analysis of video images to identify and mark nests. This method is inefficient and susceptible to human error due to fatigue, leading to missed detections and failure to identify and remove nests in a timely and accurate manner. This study addressed the challenge of intelligently detecting bird nests in high-speed railway catenaries by introducing an advanced analysis method that integrates initial detection with precise identification using deep-learning technology. Initially, the YOLO-v5 network was used to detect suspected bird nest areas from catenary monitoring images. This was followed by precise identification using the EfficientNet-B4 network. Furthermore, a comprehensive dataset comprising 15,000 images under various imaging conditions, including normal, blurred imaging, foggy conditions, partial exposure, and partial obstruction of bird nests, was constructed. The experimental results on the catenary monitoring image dataset demonstrate that the YOLO-v5 network achieves an accuracy of 0.9269 and a recall of 0.9210 on the test set. The performance is further enhanced through precise recognition using the EfficientNet-B4 network, which achieves an accuracy of 0.9479 and a recall of 0.9180. This research not only surpasses existing methods in performance but also demonstrates significant potential for application in detecting bird nests on railway catenaries. Moving beyond traditional manual identification and inspection, this study leverages deep learning to achieve the precise and rapid detection of bird nests, thereby enhancing the speed and accuracy of inspections. This advancement promotes the automation of catenary inspections and ensures the operational safety of high-speed railways. The findings are of great significance for the development of practical, intelligent detection systems for catenary bird nests in railways. © 2024 School of Science, DUTH. All rights reserved.