Revolutionizing leather quality assurance through deep learning powered precision in defect detection and segmentation by a comparative analysis of Mask RCNN and YOLO v8

The leather industry confronts persistent challenges in the maintenance of its product quality, often produced with defects that escape from the traditional conventional inspection methods. This research seeks the transformation of leather quality assurance by harnessing the power of deep learning, specifically through the comparative analysis exploration of Mask RCNN (Mask Region Convolutional Neural Network) and YOLO v8 (You Only Look Once version 8) for precise defect detection and segmentation of wet blue leather. In this comprehensive analysis, the performance of Mask RCNN and YOLO v8 are evaluated using an industrial dataset comprising various defect categories. The study encompasses a deep dive into the intricacies of each model's architecture, training processes, and inference speed to provide an obvious understanding of their respective limitations and strength. Findings from the research showcase the efficacy of both Mask RCNN and YOLO v8 in significantly enhancing defect detection accuracy compared to traditional methods. However, distinct differences emerge in terms of computational speed, resource requirements, and the models' ability to handle diverse defect sizes and shapes. This research provides valuable insights for industry practitioners, being in the selection of the most suitable model for specific operational contexts and facilitates the more informed decisions in implementation of deep learning-powered quality assurance systems. By advancing the precision of defect detection and segmentation through deep learning, the experiment results provided an inference of YOLOv8 being the model with more accuracy in detection and segmenting the defects in the leather, also its ability to provide and predict the defects using lesser training data and time, makes it more efficient model for the manufacturing of good quality of leather.