Congratulations to Farzana Islam, Sumaya, Md. Fahad Monir, and Dr Ashraful Islam for getting their paper accepted in Data in Brief, Elsevier.

The paper presents FabricSpotDefect dataset which is an annotated dataset for identifying spot defects in different fabric types.

Here is a short description of the paper:

The FabricSpotDefect dataset is, to the best of our knowledge, the first dataset specifically designed to accurately challenge computer vision in detecting fabric spots. There are a total of 1,014 raw images and manually annotated 3,288 different categories of spots. This dataset expands to 2,300 augmented images after applying six categories of augmentation techniques like flipping, rotating, shearing, saturation adjustment, brightness adjustment, and noise addition. We manually conducted annotations on original images to provide real-world essence rather than augmented images. Two versions are considered for augmented images, one is YOLOv8 resulting in 7,641 annotations and another one is COCO format resulting in 7,635 annotations. This dataset consists of various types of fabrics such as cotton, linen, silk, denim, patterned textiles, jacquard fabrics, and so on, and spots like stains, discolorations, oil marks, rust, blood marks, and so on. These kinds of spots are quite difficult to detect manually or using traditional methods. The images were snapped in home lights, using basic everyday clothes, and in normal conditions, making this FabricSpotDefect dataset established in real-world applications.

The figure below shows different spot samples with annotated bounding boxes and polygon annotation in red color 109 where (a) ink stain (b) paint spot (c) marker spot (d) makeup stain (e) rust stain (f) glue spot 110 (g)detergent stain (h) oil stain (i) coffee stain (j) food spot (k) blood spot, and (l) sweat stain.

link to download the dataset will be shared soon.