Object Detection

Ensuring the timely and accurate detection of foreign objects on railway tracks is vital for maintaining the safety and reliability of rail transport systems. This study presents an enhanced foreign object intrusion detection framework that addresses the limitations of existing methods—namely low efficiency and suboptimal accuracy—by integrating a two-stage architecture based on YOLOv8 and Overhaul Knowledge Distillation (OKD). In the first stage, a lightweight image classification model rapidly filters railway images to identify those potentially containing foreign objects, reducing the computational burden on detection models. Images flagged as suspicious are then passed to the second stage, where the YOLOv8 object detector precisely localizes and identifies the foreign objects. The use of YOLOv8 offers significant gains in both detection accuracy and inference speed over its predecessors. To further boost the performance of the classification stage, the Overhaul Knowledge Distillation technique is employed, allowing the lightweight classifier to learn from a more complex teacher network and achieve competitive accuracy with improved efficiency. Experimental evaluations confirm that the proposed approach outperforms existing solutions in both speed and robustness, establishing a new state-of