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

该数据集包含使用GoPro Hero 9 Black相机拍摄的火车车轮原始未处理图像，及其对应的用于实时车轮缺陷检测的分割标签（segmentation labels）。图像标注分为四个不同类别：车轮（Wheel）、剥离（Shelling）、变色（Discoloration）和裂纹/划痕（Cracks/Scratches）。

值得注意的是，模型会对以下类别产生混淆：剥离、裂纹和划痕。因此，我们将数据集中所有裂纹和划痕归为一个类别，称为裂纹/划痕。

标注数据：
该数据进一步按格式划分，并存储在三个文件夹中：train（训练）、test（测试）和valid（验证）。涉及的格式包括：

— JSON：位于"Labeled_data_coco_segmentation_JSON.zip"文件夹中。
— XML：位于"Labeled_data_voc_XML.zip"文件夹中。
— TXT：位于"Labeled_data_TXT.zip"文件夹中。
— TFRecord：位于"Labeled_data_tfrecord.zip"文件夹中。
— CSV：位于"labeled_data_multiclass_CSV.zip"文件夹中。

这些格式通过支持多种基于AI的模型（包括YOLO、Detectron 2、FastInst等）的训练，提升了代码的整体可用性。

有关数据集的详细标注信息，请访问以下Roboflow链接：
https://universe.roboflow.com/ncraai-mehran-university-of-engineering-and-technology-jamshoro-and-university-of-malaga-spain/wheel-defect-detection-e53jb