FaultSeg: A Dataset for Train Wheel Defect Detection

The dataset contains original raw images of train wheels captured using a GoPro Hero 9 Black camera, along with their respective segmentation labels for real-time wheel defect detection. The images are annotated for four distinct classes: Wheel, Shelling, Discoloration, and Cracks/Scratches. It is pertinent to mention here that the model confuses between following classes: peeling, cracking, and scratches. We have categorised all of the cracks and scratches in our dataset into a single class called cracks/scratches. Annotated Data: This data is further divided into formats and stored within three folders: train, test, and valid. The formats include: — JSON: Located in the “Labeled_data_coco_segmentation_JSON.zip” folder. — XML: Found in the “Labeled_data_voc_XML.zip” folder. — TXT: Available in the “Labeled_data_TXT.zip” folder. — TFRecord: Under the “Labeled_data_tfrecord.zip” folder. — CSV: Located in the “labeled_data_multiclass_CSV.zip” folder. These formats strengthen the overall usability of the code by facilitating the training of various AI-based models, including YOLO, Detectron 2, FastInst, and many others. For detailed annotation of the dataset, please go through this Roboflow link: https://universe.roboflow.com/ncraai-mehran-university-of-engineering-and-technology-jamshoro-and-university-of-malaga-spain/wheel-defect-detection-e53jb