A Dataset of Raw Fabric Grayscale Images for Defect Detection

This article presents RAW-FABRID (RAWFABricImageDataset), a publicly available annotated dataset for raw fabric defect detection using computer vision techniques. It addresses a major limitation in textile inspection, where reliance on private datasets hinders objective methodological comparisons. RAW-FABRID wasacquired using a custom-built inspection machine equipped with controlled LED illumination and aline-scan camera. Thedatasetincludesgrayscale fabric images collected from several manufacturers to ensure variability in textures and patterns. It comprises 709 high-resolution images (1792 × 1024 pixels), including both defect-free and defective samples. To maximize reusability, data are provided in two complementary formats: high-resolution images (cropped to remove peripheral acquisition artifacts) for global analysis, and a patch-based organization following the widely adopted MVTecAnomalyDetectionbenchmarkstructure. Thelatter divides images into 256 × 256pixelpatches for direct machine learning integration. Crucially, the dataset is accompanied by comprehensive metadata (CSV) and precise COCO-formatted annotations (JSON) for both subsets, ensuring full traceability and supporting object detection and semantic segmentation. The dataset is publicly available through Mendeley Data, enabling reproducible research and objective benchmarking of defect detection algorithms.