This study explores the optimization of leather defect detection through the advanced YOLOv11 model, addressing long-standing challenges in quality control within the leather industry. Traditional inspection methods, reliant on human accuracy ranging between 70% and 85%, have limited leather utilization rates and contributed to substantial material waste. To overcome these limitations, we developed an automated solution leveraging controlled environmental conditions within a custom-designed light chamber. This research specifically targets common defects in leather, such as insect larvae damage and removal cuts, by analyzing both the grain and flesh sides of the material. The results reveal a notable improvement in detection accuracy on the flesh side, achieving 93.5% for grubs and 91.8% for suckout, compared to 85.8% and 87.1% on the grain side. Classification accuracy further demonstrates the advantage of dual-side analysis, with the flesh side reaching 98.2% for grubs and 97.6% for suckout, significantly outperforming the grain side. The dual-side methodology, combined with YOLOv11’s enhanced capabilities, enables the precise identification of subtle defects and offers a transformative approach to leather defect detection. By integrating cutting-edge AI models with standardized digitization environments, this research presents a scalable, highly efficient solution that reduces human error, optimizes leather utilization, and supports industrial sustainability.
Read full abstract