Cross Position Aggregation Network for Few-Shot Strip Steel Surface Defect Segmentation

Strip steel surface defect ( $\text{S}^{3}\text{D}$ ) segmentation is a crucial method to inspect the surface quality of strip steel in the producing-and-manufacturing. However, existing $\text{S}^{3}\text{D}$ semantic segmentation methods depend on quite a few labeled defective samples for training, and generalization to novel defect categories that have not yet been trained is challenging. Additionally, some defect categories are incredibly sparse in the industrial production processes. Motivated by the above problems, this article proposed a simple but effective few-shot segmentation method named cross position aggregation network (CPANet), which intends to learn a network that can segment untrained $\text{S}^{3}\text{D}$ categories with only a few labeled defective samples. Using a cross-position proxy (CPP) module, our CPANet can effectively aggregate long-range relationships of discrete defects, and support auxiliary (SA) can further improve the feature aggregation capability of CPP. Moreover, CPANet introduces a space-squeeze attention (SSA) module to aggregate multiscale context information of defect features and suppresses disadvantageous interference from background information. In addition, a novel $\text{S}^{3}\text{D}$ few-shot semantic segmentation (FSS) dataset FSSD-12 is proposed to evaluate our CPANet. Through extensive comparison experiments and ablation experiments, we explicitly evaluate that our CPANet with the ResNet-50 backbone achieves state-of-the-art performance on dataset FSSD-12. Our dataset and code are available at (https://github.com/VDT-2048/CPANet).