用自监督重建定位高分辨率PCBA微缺陷，精度高且误报少。
Towards Pixel-Wise Anomaly Location for High-Resolution PCBA via Self-Supervised Image Reconstruction
- 设计选择性重构门与区域优化补丁选择机制，提升重建精度。
- 在500张4K图像上实现像素级缺陷定位，误报率低。
- 适合工业缺陷检测场景，代码数据将开源。
由于缺乏标注数据以及视觉复杂、高分辨率图像中仅有数像素的微缺陷，自动化印刷电路板组装件（PCBA）缺陷检测极具挑战。为此，本文提出HiSIR-Net——一种面向高分辨率PCBA像素级定位的自监督重建框架。其设计包含两个轻量模块：(i) 选择性输入-重建门（SIR-Gate），使模型自主判断何时信任重建结果而非原始输入，从而减少无关重建伪影与误报；(ii) 带位置线索的区域级优化补丁选择（ROPS）方案，实现任意分辨率下重叠补丁重建的一致性选择。两者有机结合生成清晰、高分辨率的异常图，误报率低。为填补高分辨率PCBA数据集空白，我们进一步构建了自采集的SIPCBA-500数据集，包含500张图像。在SIPCBA-500及公开基准上进行大量实验，验证了方法在定位性能上的优越性，且运行速度满足实际应用需求。论文接受后将公开完整代码与数据集。
原文摘要 · Abstract (English)
Automated defect inspection of assembled Printed Circuit Board Assemblies (PCBA) is quite challenging due to the insufficient labeled data, micro-defects with just a few pixels in visually-complex and high-resolution images. To address these challenges, we present HiSIR-Net, a High resolution, Self-supervised Reconstruction framework for pixel-wise PCBA localization. Our design combines two lightweight modules that make this practical on real 4K-resolution boards: (i) a Selective Input-Reconstruction Gate (SIR-Gate) that lets the model decide where to trust reconstruction versus the original input, thereby reducing irrelevant reconstruction artifacts and false alarms; and (ii) a Region-level Optimized Patch Selection (ROPS) scheme with positional cues to select overlapping patch reconstructions coherently across arbitrary resolutions. Organically integrating these mechanisms yields clean, high-resolution anomaly maps with low false positive (FP) rate. To bridge the gap in high-resolution PCBA datasets, we further contribute a self-collected dataset named SIPCBA-500 of 500 images. We conduct extensive experiments on our SIPCBA-500 as well as public benchmarks, demonstrating the superior localization performance of our method while running at practical speed. Full code and dataset will be made available upon acceptance.
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