Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection

2026-06-02

AI Summary

This study addresses the challenge of detecting subtle, low-contrast defects embedded within dense backgrounds in printed circuit board (PCB) inspection. To this end, the authors propose a two-stage detection framework: first, a structure-guided hybrid masked sparse convolution is employed for pretraining to learn structural priors inherent to PCB layouts; second, during fine-tuning, a spatial continuity regularization is introduced to enhance the compactness and coherence of predictions for elongated defect regions. By innovatively integrating structural prior modeling with spatial constraints, the method achieves state-of-the-art performance on the DsPCBSD+ dataset, attaining 85.5% mAP₀.₅ and 52.3% mAP₀.₅:₀.₉₅, significantly outperforming existing strong baselines.

Abstract

Printed circuit board (PCB) defect detection is an essential part of automated optical inspection (AOI); yet it remains challenging in practice because many defects are tiny, low-contrast, and embedded in dense circuit backgrounds. To address these issues, this paper presents a two-phase PCB defect detection framework that combines structure-guided mixed masked pretraining with spatial continuity regularization. In the pretraining stage, we design a sparse convolutional masked pretraining scheme to exploit unlabeled PCB images, where structure-guided mixed masking is used to construct informative masked inputs. The sparse convolutional reconstruction pipeline suppresses invalid responses from masked regions and enables the detector backbone to infer missing PCB structures from visible conductive patterns, thereby learning PCB structural priors. In the fine-tuning stage, the pretrained backbone is transferred to the downstream defect detection task. For the task, a spatial continuity regularization term is introduced during fine-tuning. This term constrains dispersed positive predictions assigned to the same defect instance and promotes more compact localization on elongated defect regions. Experiments on the DsPCBSD+ dataset show that the proposed method achieves 85.5% mAP0.5 and 52.3% mAP0.5:0.95, outperforming several strong baseline detectors. Ablation studies and qualitative results further confirm the effectiveness of the proposed framework for robust PCB defect detection in industrial AOI scenarios.

Problem

• PCB defect detection
• automated optical inspection
• low-contrast defects
• tiny defects
• dense circuit backgrounds

Innovation

• structure-guided masked pretraining
• sparse convolutional reconstruction
• spatial continuity regularization
• PCB defect detection
• mixed masking