Experimental Study on YOLO-Based Leather Surface Defect Detection

Abstract

Accurate, reliable, and fast intelligent detection of leather surface defects has become an important subject in industrial inspection, which aims at improving production efficiency and increasing automation levels. This work focuses on the rapid defect recognition and localization of leather surface defects for industrious applications, which is based on the state-of-the-art real-time detection model YOLO. Three experimental Schemes with different challenges were designed to find the optimal YOLO-based leather surface defect detection scheme. Typical tanned leather surface defect images from the factory were collected, which are comprised of eight types of defects, namely rotten surface, hole, scratch, crease, healed injury, bacterial injury, growth line, and pinhole, which exhibit variations in shapes, sizes, and colors, reflecting the various characteristics found in tanned leather defects. A comprehensive and in-depth review of the YOLO series of models is presented, including YOLOv1 to YOLOv8. The systematic and extensive experiments were conducted,which indicate that the YOLO models can simultaneously detect multiple types of defects present in each leather image. The multi-defect detection task achieved a maximum of 52.3% mean average precision (mAP), 58.2% precision, and 68.7% recall. For single-class detection tasks, the highest performance reached 85.1% mAP, 90.9% precision, and 81.8% recall. These works provide feasible intelligent solutions for surface defects in the leather industry, laying a solid foundation for the design and development of new solutions for leather defect detection.

Keywords

• Leather
• defect detection
• YOLO
• deep learning