FabricSpotDefect: An Annotated Dataset for Identifying Spot Defects in Different Fabric Types

Content: This FabricSpotDefect dataset is designed to improve fabric spot defect detection using computer vision. It includes various types of fabrics such as cotton, linen, silk, denim, and both plain and patterned textiles, featuring spot defects like stains, discolorations, oil marks, and more. Format: The images are in 2D RGB .jpg format of varying sizes. Original Images: 1,014 raw images with 3,286 spot defect labels. Augmented Images: After applying six types of augmentation techniques, the dataset expands to 2,300 images with 7,641 labeled spots. Data Features: The dataset features both original and augmented images organized into “original” and “augmented” folders, each containing subfolders for training, validation, and testing. All images are resized to 416×416 pixels and undergo six augmentation techniques: flipping, rotation, shearing, saturation, brightness, and noise addition. How data are acquired: The FabricSpotDefect dataset images were collected from everyday home fabrics under natural lighting using three smartphones( Samsung Galaxy Note20, Samsung Galaxy S20 FE, and Samsung Galaxy A53 5G). Use Case: To develop an AI model that will identify spot defects in fabrics, so that the model could be used to improve the quality control process in textile production.内容：本织物斑点缺陷（FabricSpotDefect）数据集旨在借助计算机视觉技术提升织物斑点缺陷检测性能。数据集涵盖棉、麻、丝、牛仔布等多种面料类型，以及平纹、印花等不同织物品类，包含污渍、变色、油迹等各类斑点缺陷。 图像格式：所有图像均为2D RGB .jpg格式，尺寸各不相同。 原始图像：共1014张原始图像，附带3286个斑点缺陷标注。 增强图像：通过六种数据增强技术处理后，数据集扩充至2300张图像，总计7641个斑点标注。 数据特征：数据集包含原始图像与增强图像，分别存储于"original"与"augmented"文件夹中，每个文件夹下均设有训练、验证与测试子集。所有图像均被统一调整至416×416像素，且采用六种增强手段进行处理：翻转、旋转、剪切、饱和度调整、亮度调整以及噪声添加。 数据采集方式：FabricSpotDefect数据集的图像均通过三款智能手机（三星Galaxy Note20、三星Galaxy S20 FE以及三星Galaxy A53 5G）在自然光照环境下，采集自日常家用织物。 应用场景：用于开发可识别织物斑点缺陷的人工智能模型，以优化纺织品生产中的质量控制流程。