MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation
- Resource Type
- Conference
- Authors
- Lin, Dongyun; Cheng, Yi; Li, Yiqun; Prasad, Shitala; Guo, Aiyuan
- Source
- 2022 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2022 IEEE International Conference on. :441-445 Oct, 2022
- Subject
- Computing and Processing
Signal Processing and Analysis
Training
Image segmentation
Product design
Decoding
Quality assessment
Task analysis
Defect Segmentation
UNet
Multi-Level Spatial Attention
- Language
- ISSN
- 2381-8549
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.