Locally Aware Visual State Space for Small Defect Segmentation in Complex Component Images

Segmenting small defects within large imaging fields remains challenging in industrial scenarios due to the difficulty in distinguishing defects from complex component backgrounds and identifying defects comprising only a few pixels in high-resolution images. To address these issues, we propose a novel dual-branch feature extraction architecture, the locally aware visual state space block, which captures global contextual information while maintaining locally aware perception. In addition, we introduce the parallel quad-directional scanning fusion module to extract multiscale information, aggregating high-level features at different scales for enhanced global information fusion. To avoid losing small target details when upsampling the global segmentation mask to high-resolution input size, we develop progressive location refinement modules to incrementally refine small defect localization from the bottom up. Extensive experiments on our proposed small defect segmentation dataset and a public PCB dataset demonstrate that our method outperforms existing state-of-the-art methods in both performance and efficiency.