D. Lin, Y. Cheng, Y. Li, S. Prasad and A. Guo, MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 441-445, doi: 10.1109/ICIP46576.2022.9897416.
Defect segmentation from 2D images plays a critical role in industrial product quality assessment. In practice, it is common that there are sufficient normal (defect-free) images but a very limited number of anomalous (defective) images. The existing works proposed several UNet variants (e.g., CAM-UNet) by incorporating normal images into the training process to improve the defect segmentation performance. In this paper, we propose Multi-Level Spatial Attention UNet (MLSA-UNet) to address the industrial defect segmentation task. MLSA-UNet is trained in an end-to-end manner to simultaneously classify normal/anomalous images and segment out defective regions from anomalous images. The classification process is conducted by Spatial Attention Learning Module (SALM) to generate multi-level spatial attention maps which are exploited by Spatial Attention Guided Decoding Module (SADM) to provide the guidance in the decoding process of UNet. Extensive experiments on MVTec AD dataset demonstrate the superiority of the proposed MLSA-UNet over multiple state-of-the-art UNet variants on defect segmentation.
There was no specific funding for the research done