Version 1
: Received: 4 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (11:06:33 CET)
How to cite:
Banduka, N.; Tomić, K.; Živadinović, J.; Mladineo, M. Enhancing Automated Defect Detection and Classification using YOLOv11: Case Study of Finished Leather industry. Preprints2024, 2024110239. https://doi.org/10.20944/preprints202411.0239.v1
Banduka, N.; Tomić, K.; Živadinović, J.; Mladineo, M. Enhancing Automated Defect Detection and Classification using YOLOv11: Case Study of Finished Leather industry. Preprints 2024, 2024110239. https://doi.org/10.20944/preprints202411.0239.v1
Banduka, N.; Tomić, K.; Živadinović, J.; Mladineo, M. Enhancing Automated Defect Detection and Classification using YOLOv11: Case Study of Finished Leather industry. Preprints2024, 2024110239. https://doi.org/10.20944/preprints202411.0239.v1
APA Style
Banduka, N., Tomić, K., Živadinović, J., & Mladineo, M. (2024). Enhancing Automated Defect Detection and Classification using YOLOv11: Case Study of Finished Leather industry. Preprints. https://doi.org/10.20944/preprints202411.0239.v1
Chicago/Turabian Style
Banduka, N., Jovan Živadinović and Marko Mladineo. 2024 "Enhancing Automated Defect Detection and Classification using YOLOv11: Case Study of Finished Leather industry" Preprints. https://doi.org/10.20944/preprints202411.0239.v1
Abstract
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.
Keywords
finished leather; YOLO; classification; detection; deep learning; computer vision
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
