MLR-Net: A multi-layer residual convolutional neural network for leather defect segmentation

Abstract

It is critical to recognize anomalous features in wet-blue leather samples as part of industrial quality control in the leather industry. In industrial settings, manual inspection of leather samples is the present practice. Visual inspection-based leather processing is required to meet current industry requirements that encourage large-scale automation. Visual assessment of uneven surfaces is a difficult subject since irregularities can assume many different shapes and colors. The objective of this research is to develop an automated system that can detect the defects of leather images based on visual surface analysis. A deep learning-based strategy is developed to accomplish this goal, which trains the system to identify uneven and regular leather surfaces and classify leather images based on those surfaces. To address the task of defect segmentation in leather images, we introduce MLR-Net, a multi-layer residual convolutional neural network. Our proposed MLR-Net demonstrates promising performance in this task, achieving competitive results on our curated leather images dataset. The evaluation of MLR-Net reveals average sensitivity of 87.50%, specificity of 99.78%, an accuracy of 95.98%, F1-score of 85.90%, and Intersection over Union (IoU) of 78.98% respectively. These metrics indicate the effectiveness of MLR-Net in accurately identifying and segmenting defects in leather images.