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Published July 26, 2024 | Version v1
Data paper Restricted

FaultSeg: A Dataset for Train Wheel Defect Detection

Authors/Creators

  • 1. University of Malaga
  • 2. ROR icon Mehran University of Engineering and Technology
  • 3. ROR icon Torrens University Australia

Description

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.

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Additional details

Related works

Is part of
Journal article: 10.1109/ACCESS.2023.3240167 (DOI)

Funding

Higher Education Commission
NCRA-CMS Lab 2(1076)/HEC/M&E/2018/704

Dates

Collected
2023-01-25
Data Collection

Biodiversity

Event date
2023-01-25 , 2023-01-26
Individual count
5
Country
Pakistan

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