Symmetry 2026, 18(6), 969; https://doi.org/10.3390/sym18060969 (registering DOI) - 3 Jun 2026
Abstract
►
Show Figures
To address the issues in existing PCB defect detection models, including insufficient capability for capturing small defects, weaker global feature modeling, and inadequate multi-scale feature fusion, this paper proposes a C2f-FPN-PAN++-Mamba model based on an improved YOLOv8n. The Mamba state–space model is embedded
[...] Read more.
To address the issues in existing PCB defect detection models, including insufficient capability for capturing small defects, weaker global feature modeling, and inadequate multi-scale feature fusion, this paper proposes a C2f-FPN-PAN++-Mamba model based on an improved YOLOv8n. The Mamba state–space model is embedded into the C2f module to construct a C2f-Mamba feature extraction unit, which, while retaining the local perception capability of convolution, enhances long-range dependency modeling, accurately capturing global semantic information of subtle defects in complex backgrounds and significantly improving the model’s feature representation ability for small defects. Meanwhile, an FPN-PAN++ enhanced feature fusion structure is introduced, achieving efficient complementary interaction between high and low-level features through bidirectional cross-scale feature aggregation and path augmentation, thereby strengthening the model’s robustness in identifying multi-scale and multi-form defects. Finally, the C2f-Mamba and FPN-PAN++ are organically integrated, improving global modeling and multi-scale fusion capabilities while maintaining lightweight computational efficiency, effectively reducing the miss and false detection rates of small defects. Experimental results indicate that, compared with the original YOLOv8n model, the proposed method achieves significant performance improvements in PCB defect detection tasks. On the PCB defect dataset, the model’s precision increased from 96.4% to 98.5%, recall from 94.6% to 98.4%, and mAP@0.5 from 97.2% to 98.8%, with the mAP@0.5:0.95 metric, reflecting multi-scale detection performance, rising dramatically from 57.5% to 62.5%. Experiments demonstrate that this method effectively enhances detection capability for small and complex defects while preserving the advantages of a lightweight model and high inference speed, providing a reliable technical solution for high-precision, real-time PCB defect detection in industrial scenarios.
Full article
Figure 1
