MAFF-Net: PCB defect detection via feature-enhancement fusion

Jian Chen, Shouyin Wang, Ziyuan Chen, Wenzhong Huang, Li Lin, Huiqun Hong, Geng Chen, Zhuwu Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

Defect detection of printed circuit board (PCB) is of significant practical importance to ensure quality control in the production process. However, traditional defect detection methods suffer from limitations such as low detection accuracy and poor generalization ability. To tackle these issues, we propose a novel deep learning-based defect detection method for bare PCBs through multi-attention adaptive feature-enhancement fusion (AFF). First, we utilize ResNext101 as the backbone for feature extractor and embed a normalization-based attention mechanism in a residual structure, aiming at improving the feature extraction capability of the network. Second, we introduce an AFF module, which leverages multi-scale feature extraction and feature fusion to facilitate information interaction and enhance the correlation of feature information between channels. Finally, we incorporate the coordinate attention mechanism into AFF to highlight the target area for boosting detection accuracy. The experimental results demonstrate the effectiveness of the proposed method, which achieves a mean accuracy precision (mAP) of 99.01 % on a publicly available PCB defect dataset.

Original languageEnglish
Article number026137
JournalMeasurement Science and Technology
Volume36
Issue number2
DOIs
StatePublished - 28 Feb 2025

Keywords

  • attention mechanism
  • deep learning
  • defect detection
  • feature-enhancement fusion
  • PCB

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