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CM-UNetv2: An Enhanced Semantic Segmentation Model for Precise PCB Defect Detection and Boundary Restoration.

Qiyang Guo¹, Yajun Chen¹, Yirui Zhu¹

¹Department of Information Science, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

CM-UNetv2 enhances printed circuit board (PCB) defect detection using a novel semantic segmentation network. This advanced method significantly improves the identification of small defects and boundary accuracy for better electronic product quality.

Keywords:

PCB multi-scale feature extraction neural networks semantic segmentation

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Area of Science:

Electronics Manufacturing
Computer Vision
Artificial Intelligence

Background:

Printed circuit boards (PCBs) are crucial in electronics, requiring precise defect detection for quality assurance.
Traditional methods struggle with small, irregularly shaped PCB defects amidst complex textures.
Automated defect detection is vital for reliability and manufacturing efficiency.

Purpose of the Study:

To introduce CM-UNetv2, a novel semantic segmentation network for enhanced PCB defect detection.
To address the challenges of detecting small, complex defects in PCB manufacturing.
To improve the accuracy and robustness of automated PCB inspection systems.

Main Methods:

Developed CM-UNetv2, a semantic segmentation network with three innovative architectural modules.
Incorporated Parallelized Patch-Aware Attention (PPA) for multi-scale feature representation.
Introduced Dual-Stream Skip Guidance (DSSG) for refined semantic and spatial information preservation.
Designed Frequency-domain Guided Context Mamba (FGCMamba) with Spatial Guidance Cross-Attention (SGCA) and Frequency-domain Self-Attention Solver (FSAS) for improved boundary restoration and efficiency.

Main Results:

CM-UNetv2 achieved state-of-the-art performance on MeiweiPCB and KWSD2 datasets.
Demonstrated superior accuracy in detecting small PCB defects.
Showcased enhanced boundary accuracy and overall segmentation robustness compared to existing methods.
Validated the effectiveness of PPA, DSSG, and FGCMamba modules in improving detection capabilities.

Conclusions:

CM-UNetv2 offers a significant advancement in automated PCB defect detection.
The proposed architectural innovations effectively address the limitations of traditional methods.
The network provides a robust and accurate solution for ensuring electronic product quality and reliability.