Real-time detection of railway cracks based on improved YOLOX-Nano

Abstract

Cracks in the rails will lead to great safety hazards in railway transportation. Aiming at the problems of low detection accuracy and inconspicuous part of cracks in crack detection, an improved model based on YOLOX-Nano is proposed. The SA-NET lightweight combined attention mechanism is added to the model to generate a feature map with channel attention and spatial attention, which strengthens the model's attention to target features and location information. Secondly, use Alpha-CIoU Loss to replace IoU Loss to increase the accuracy of the model's prediction box. The comparison experiment was carried out on the self-built data set, and the mAP of the improved YOLOX-Nano model reached 77.58%, the detection speed reached 42.2FPS, and the calculation amount and parameter amount of the model were only 0.508G and 3.5MB respectively, and the overall performance was better than other models.