The study on multi-defect detection for leather using object detection techniques

Abstract

Leather has played a very important role in human life for thousands of years. Ensuring the quality of leather and addressing surface defects poses significant challenges. Traditionally, human inspectors are responsible for detecting surface defects in tanneries, but this approach is labor-intensive and susceptible to human error. As a result, there is a growing demand for automated systems to detect the defects. Herein, artificial intelligence (AI) was developed to detect the defects on leather surfaces. Six targeted defect types, denoted as insect bites, scratches, holes, stitch marks, diseased and ruptures, were specifically addressed to enhance the overall quality assessment process. AI-based vision techniques were used to detect flaws on the leather on photographs taken with a high-resolution camera. Deep learning algorithms Mask R-CNN, YOLOv8 and within the framework of Detectron2, RetinaNet R101 3x, Faster R-CNN R101-FPN 3x models were performed and a comparison was made between these algorithms. By using the slicing aided hyper-inference (SAHI) algorithm in coordination with these algorithms, the detection rates of small defects on the images were increased. The highest accuracy rate was achieved when the YOLOv8 algorithm had 75 epoch values for training, and the SAHI algorithm had slice height-width values of 256 × 256 pixels.

Graphical Abstract