Detección automatizada de defectos en cuero wet blue mediante visión artificial con YOLOv8s en Curtiembre

The manual inspection of Wet Blue leather in the tanning industry is prone to inconsistencies, resulting in significant economic losses. This study addresses this issue by developing and validating a computer vision system for automated defect detection and classification. A YOLOv8s model was trained using a dataset of 4,158 images captured under real operational conditions in a local tannery, classifying defects into four categories: good, veins, fat nodules, and holes. The system incorporates post-processing logic to assign a final quality grade (First, Second, Third) according to an industrial rubric. Results demonstrate high effectiveness, with an overall mean Average Precision ([email protected]) of 98.2% for defect detection on a validation set, and 96.3% classification accuracy on a test set for quality grading. The model operates at 30 frames per second (FPS) on consumer-grade hardware, confirming its viability for real-time deployment. We conclude that the YOLOv8s-based system offers a reliable and efficient solution to standardize quality control, reduce human error, and optimize product value in the leather industry.