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FaultSeg: A Dataset for Train Wheel Defect Detection

Type de record:

dataset
Créateur:
ShaJatNavCho
Éditeur:
Zenodo
Hôte:avatar
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.

Visit

doi.orgzenodo.org

Tasks

computer visionimage classification

Tags

Data processingImage ProcessingPattern recognitionImage recognitionReal-time image recognitionAutomationWheelsetRailwayTransportationArtificial Intelligence

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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