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

Jinghao Bian, Mingtao Feng, Weisheng Dong, Zijie Wu, Jianqiao Luo, Fangfang Wu, Yaonan Wang, Guangming Shi

Abstract: 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.