Rail-5k: a Real-World Dataset for Railway Surface Defects Detection

To encourage research in computer vision for the railway, we present Rail-5k: a real-world image dataset for object detection of defects and accessories on the rail, along with methods for shooting, fine-frained category definition, and instance-level annotation.<br> We collected 5,000 high-quality RGB images from high-speed railway and subway across China, where each image with resolution as high as 0.03mm per pixel. <br> We annotate 1100 images with 13 types of defects and accessories that are the most important to rail maintenance such as rail surface, wheel-rail contact band, crack, spalling, corrugation, fastening, screw.<br> The dataset is superior to existing datasets in image quantity, resolution, annotation quality, dense and small objects. <br> It also contains real-world corrupted images with dark, overexposure, blur, other tools, different lens distance, category transition, different screws, which are infeasible for non-experts to annotate and recognize.<br> As a pilot study of rail defect detection, we perform comprehensive experiments using SOTA models.<br> Our experiments demonstrate several challenges Rail-5k posed to both computer vision and railway engineering. Future versions of this dataset will include even more images, segmentation annotations as well as more channels.