PCB-FS: Frequency–Spatial Feature Learning for PCB Defect Detection

Printed circuit board (PCB) defect detection is essential for ensuring manufacturing quality and product reliability in electronics production. PCB designs often exhibit inherent symmetry in circuit layouts and periodic patterns, which defects disrupt by introducing asymmetries detectable in spatial and frequency domains. Contemporary methods predominantly emphasize either global context modeling or local detail preservation, yet fail to synergistically leverage their complementary characteristics. Existing approaches predominantly constrain operations to the spatial domain, neglecting complementary frequency-domain representations that encode periodic structures and spectral anomalies. We present PCB-FS, a unified frequency–spatial learning framework that augments YOLOv8 with three synergistic components: (i) Dual-Domain Convolution (DD-Conv) for adaptive spatial frequency feature extraction, (ii) Global–Local Axial Attention (GLA-Attention) for context-aware defect modeling, and (iii) Cross-Stage Partial Dynamic Shifted Large Kernel Convolution (C2f-DSLKConv) for efficient large-receptive-field fusion. Our DD-Conv module adaptively fuses spatial and frequency-domain representations, while the GLA-Attention mechanism enhances global context modeling without sacrificing local detail preservation. The C2f-DSLKConv module further enables efficient large-receptive-field spatial modeling and hierarchical feature aggregation. Our method exploits symmetry-breaking artifacts in PCB layouts to detect structural anomalies, achieving superior defect localization accuracy. A comprehensive evaluation on the enhanced PCB surface defect dataset (DsPCBSD+) demonstrates that PCB-FS achieves 86.2% mAP@50 and 52.4% mAP@50-95, establishing new state-of-the-art performance with significant margins over existing methods. Integrating frequency and spatial domain features significantly enhances PCB defect detection reliability and efficiency in practical applications.