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.

The paper presents a study on optimizing leather defect detection using the YOLOv11 model. Traditional leather processing has faced challenges in quality control, where human inspection accuracy ranges between 70% to 85%, affecting leather utilization rates and resulting in significant material waste. This research introduces an automated solution to improve defect classification and detection accuracy. The study focuses on defects commonly found in leather, such as insect larvae damage and removal cuts, using a specialized light chamber to control environmental variables. By analyzing both grain and flesh sides of the leather, the researchers demonstrated a significant increase in defect detection accuracy, with the flesh side achieving a higher classification rate. YOLOv11's dual-side analysis allowed for clearer identification of subtle defects, leading to a more efficient classification process. The results suggest that integrating advanced AI models like YOLOv11 with controlled digitization environments could drastically reduce human error and improve leather utilization, providing a scalable solution for the leather industry.