A CAM-Based Weakly Supervised Method for Surface Defect Inspection

Product surface defect detection is vital to ensure the quality, efficiency, and reliability of industrial production. Deep-learning-based product surface defect inspection algorithms have been gradually used because of their higher detection accuracy and better generalization performance. However, the current deep-learning-based algorithms require a large amount of training samples and high-cost manual annotation work, which is inefficient and costly. In this article, we propose a weakly supervised defect segmentation algorithm of image-level labels based on a classification activation map (CAM). First, we use a Siamese network to narrow the gap between image-level and pixel-level supervision. Then, three modules are improved to enhance the inspection performance, i.e., auto-focused subregion loss, max-pooling-based nonlocal attention, and log summation exponential global pooling, which are used to boost the segmentation without additional computation complexity. To evaluate the performance of the proposed method, we conduct comparison experiments on two public datasets: Deutsche Arbeitsgemeinschaft fuer Muster-erkennung (DAGM) and KolektorSDD. The experimental results showed that the proposed method is superior and generalized than state-of-the-art weakly supervised methods. Furthermore, our method outperforms some early fully supervised segmentation algorithms.