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A lightweight object detection algorithm for railway crossing embedded devices

• 1、Yankuang Logistics Technology Co.， Ltd.， Zoucheng， Shandong 273500， China
• 2、College of Electrical Engineering and Automation， Shandong University of Science and Technology， Qingdao， Shandong 266590， China

Abstract：To address the difficulty in deploying object detection algorithms on embedded devices at railway crossings， a lightweight algorithm， namely YOLO-ED， was proposed.The YOLOV5 model was optimized through the introduction of a novel C3-Faster module， a coordinate attention mechanism， and Focal-EIoU loss function， aiming at achieving a balance between detection accuracy and computational efficiency. The experimental results demonstrate that， in comparison with the baseline model， the inference speed of YOLO-ED is increased by 26.9% while maintaining a comparable level of detection accuracy， with the parameter count and computational load significantly reduced by 27.3% and 33.3%， respectively. The successful deployment of the algorithm on the Jetson Nano B01 platform further validates its high efficiency and practical engineering value in resource-constrained scenarios.

Keywords： railway level crossing； embedded systems； lightweight object detection algorithm； YOLO