Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX

Abstract

During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.