Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background
<|reference_start|>Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background: Despite the eye-catching breakthroughs achieved by deep visual networks in detecting region-level surface defects, the challenge of high-quality pixel-wise defect detection remains due to diverse defect appearances and data scarcity. To avoid over-reliance on defect appearance and achieve accurate defect segmentation, we proposed a change-aware Siamese network that solves the defect segmentation in a change detection framework. A novel multi-class balanced contrastive loss is introduced to guide the Transformer-based encoder, which enables encoding diverse categories of defects as the unified class-agnostic difference between defect and defect-free images. The difference presented by a distance map is then skip-connected to the change-aware decoder to assist in the location of both inter-class and out-of-class pixel-wise defects. In addition, we proposed a synthetic dataset with multi-class liquid crystal display (LCD) defects under a complex and disjointed background context, to demonstrate the advantages of change-based modeling over appearance-based modeling for defect segmentation. In our proposed dataset and two public datasets, our model achieves superior performances than the leading semantic segmentation methods, while maintaining a relatively small model size. Moreover, our model achieves a new state-of-the-art performance compared to the semi-supervised approaches in various supervision settings.<|reference_end|>
@article{liu2024change-aware,
    title={Change-Aware Siamese Network for Surface Defects Segmentation under
  Complex Background},
    author={Biyuan Liu, Huaixin Chen, Huiyao Zhan, Sijie Luo, Zhou Huang},
    journal={arXiv preprint arXiv:2409.00589},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00589},
    primaryClass={cs.CV}
}