GRA-Net: Global receptive attention network for surface defect detection

Abstract

Surface defect detection is a key link in the production process of industrial products. However, it is still challenging to realize automatic detection due to the low contrast between the defect and background, the random spatial positions and shapes of defects, and the imbalance between positive and negative samples. Although convolutional neural network (CNN) has been successfully applied to defect detection, current CNN-based methods are still unable to cope with the above challenges because of the insufficient global representation ability, small receptive field, and difficulty in capturing tiny targets. To address these problems, this paper proposes a global receptive attention network (GRA-Net) for surface defect detection. This deep learning system introduces a backbone network combining CNN and transformers for feature extraction, so defects with low contrast can be identified. Then, a multi-level receptive field module is designed to achieve the overall perception for complex and changeable surface defects. Furthermore, a feature interaction attention module is developed to maximize the utilization of the comparatively few defect pixels. Finally, the defect boundary is refined by an adaptive boundary refinement block. Semantic segmentation experiments are conducted based on three datasets. The results show that the mean intersection-over-union and pixel accuracy of GRA-Net are 4–7 % and 2–5 % higher than the existing models, respectively.