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

本数据集包含使用GoPro Hero 9 Black相机采集的火车车轮原始实拍图像，以及用于实时车轮缺陷检测的对应分割标注标签。图像共标注了四类目标类别：车轮（Wheel）、剥离（Shelling）、变色（Discoloration）以及裂纹/划痕（Cracks/Scratches）。

在此需特别说明：部分模型易对以下类别产生混淆：起皮（peeling）、开裂（cracking）与划痕（scratches），因此本数据集将所有裂纹与划痕归为统一的裂纹/划痕（Cracks/Scratches）类别。

标注数据说明：
该标注数据进一步按标注格式分类，并存储于train、test、valid三个文件夹中，各标注格式如下：
—— JSON格式：存储于"Labeled_data_coco_segmentation_JSON.zip"压缩包内。
—— XML格式：收录于"Labeled_data_voc_XML.zip"压缩包中。
—— TXT格式：可于"Labeled_data_TXT.zip"压缩包中获取。
—— TFRecord格式：位于"Labeled_data_tfrecord.zip"压缩包内。
—— CSV格式：存储于"labeled_data_multiclass_CSV.zip"压缩包中。

上述多种标注格式可适配包括YOLO、Detectron 2、FastInst在内的各类AI模型训练需求，大幅提升数据集的代码适配性与易用性。

如需了解本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

搜集汇总

背景与挑战

背景概述

FaultSeg是一个用于火车车轮缺陷检测的数据集，包含原始图像和分割标签，标注了车轮、剥落、变色和裂纹/划痕四个类别。数据以多种格式存储，支持训练不同类型的AI模型。