UniPCB: A Generation-Assisted Detection Framework for PCB Defect Inspection

TL;DR

UniPCB introduces a joint generation and detection framework for PCB defect inspection, effectively addressing data scarcity and complex backgrounds in IIoT environments through controlled defect synthesis and advanced detection architectures.

Contribution

The paper presents a novel integrated framework combining defect synthesis with detection, enhancing PCB defect inspection performance in IIoT settings.

Findings

Achieved 98.0% mAP@0.5 in defect detection, surpassing existing methods.

Generated samples with an FID of 129.61 and SSIM of 0.619, outperforming prior generation approaches.

Enhanced detection performance by augmenting training data with synthesized defect samples.

Abstract

In the Industrial Internet of Things (IIoT), enabling intelligent, real-time Printed Circuit Board (PCB) defect inspection is critical for ensuring product reliability. However, existing IIoT-based visual inspection systems face two compounding challenges: scarce and imbalanced defect samples that limit model training, and insufficient feature representation under complex circuit backgrounds. Existing generation methods rely on single-modality conditions with coarse structural control, while detection methods improve architectures without addressing the data bottleneck. To resolve both challenges jointly, we propose a generation-assisted PCB defect inspection framework that integrates controlled defect synthesis with task-specific defect detection within an IIoT-enabled pipeline. On the generation side, a Multi-modal Condition Generator extracts complementary edge, depth, and text conditions in parallel. A ScaleEncoder then embeds these conditions into the diffusion U-Net at four resolutions, and a Condition Modulation applies FiLM-style spatially-adaptive modulation at each scale, enabling structurally aligned and defect-aware sample synthesis to augment the scarce IIoT dataset. On the detection side, an Inverted Residual Shift Attention couples self-attention with shift-wise convolution to jointly capture global context and local texture, and a Cross-level Complementary Fusion Block generates pixel-level gates for selective cross-level feature fusion. The synthesized samples directly enrich the detection training set, so that improvements in generation compound with improvements in detection. Extensive experiments on DsPCBSD+ demonstrate that UniPCB achieves mAP@0.5 of 98.0% and mAP@0.5:0.95 of 61.8% on defect detection, surpassing all compared methods, while the generation branch attains an FID of 129.61 and SSIM of 0.619, outperforming existing conditional generation approaches.