Enhancing defective region visualization in industrial products using Grad-CAM and random masking data augmentation

Abstract

Defect detection in various industrial products ensures product quality and safety. This paper introduces an innovative design, training, and evaluation application employing CNN, CAE, YOLO, FCN, and SVM models, to facilitate defect detection without requiring extensive IT expertise. However, conventional usage of Grad-CAM for visualizing defect regions sometimes includes irrelevant areas unrelated to the target defects. A novel data augmentation technique called random masking is proposed to enhance the visualization of defective regions, leading to more accurate and focused defect detection in various industrial products. This technique is used during training, replacing non-target areas in each image with randomly generated mask patterns. The efficacy of the proposed technique is demonstrated through visualization tests of defective regions using Grad-CAM. Furthermore, an ablation study is conducted to assess the effectiveness of the data augmentation techniques, comparing the performance of Grad-CAM with and without random masking augmentation. We further provide insights into the dataset used and present noteworthy findings from the evaluation, showcasing the contributions of our work in advancing defect detection methodologies.