Fabric Defects Object Detection Dataset

The dataset comprises 720 images each depicting four distinct defect types in colored fabrics: Oil, Hole, Cutting, and Crack, totaling approximately 180 images per type. This dataset holds significant academic value, particularly within the realm of Computer Vision, serving as a crucial resource for developing image processing algorithms and deep learning models for tasks such as classification, object detection, or segmentation. Its application stands to significantly enhance advancements in textile engineering and manufacturing processes.<br>The FD_Dataset comprises two distinct files:<br><br>FD, which encompasses comprehensive datasets alongside Label.mat facilitated through MATLAB for any object detection model.YOLO, housing datasets for training, validation, and testing specifically tailored for YOLO object detection models.<br>

本数据集共包含720张彩色织物瑕疵图像，涵盖四类典型织物缺陷：油渍（Oil）、破洞（Hole）、切边瑕疵（Cutting）与裂纹（Crack），每类缺陷约含180张图像。本数据集具备极高学术价值，尤其在计算机视觉（Computer Vision）领域中，可作为开发图像处理算法与深度学习模型的关键资源，适用于图像分类、目标检测或语义分割等任务，其应用可有效推动纺织工程与制造工艺的技术进步。
FD数据集（FD_Dataset）包含两类独立文件：

其一为FD文件包，整合了完整数据集与配套的Label.mat标注文件，可通过MATLAB工具加载，适配各类目标检测模型；其二为YOLO文件包，专为YOLO目标检测模型打造，包含用于模型训练、验证与测试的数据集。

搜集汇总

背景与挑战

背景概述

该织物缺陷目标检测数据集包含720张图像，覆盖4种典型缺陷类型（每种约180张），提供MATLAB和YOLO两种格式的标注数据，专门用于开发纺织品缺陷检测的计算机视觉算法。