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<p>The surface defects of printed circuit boards (PCB) that occur during the manufacturing process seriously affect product quality. So it is important to detect PCB surface defects quickly and accurately. However, existing defect detection methods still have room to be improved for PCB surface defect detection. This paper proposes an advanced model, MDUA-YOLO, based on YOLOv5 to increase the detection accuracy of defects on PCB surface. Firstly, we introduce the C3 Mobile Vision Transformer (C3MobileViT) module in the backbone of YOLOv5, improving the feature extraction capability of the model. Secondly, the Deformable Convolutional-Receptive Field Block (DC-RFB) module is incorporated into the neck of YOLOv5 to dynamically expands the receptive field and more accurately capture the location information of small defects. Additionally, we design the Union Attention Block (UAB) module in the neck of YOLOv5 to optimize the fusion of low-level and high-level feature maps. Finally, an extra prediction head and new feature fusion layer are also added to enhance the ability of the model to detect small defects. On the benchmark PKU-PCB and DeepPCB datasets, numerous experimental results show that the MDUA-YOLO surpasses other comparison state-of-the-art models and meets the real-time detection requirement of industrial environment.</p>

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MDUA-YOLO: an advanced deep learning approach for PCB surface defect detection

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Xiaowei Liu,
  • Haichao Wang

摘要

The surface defects of printed circuit boards (PCB) that occur during the manufacturing process seriously affect product quality. So it is important to detect PCB surface defects quickly and accurately. However, existing defect detection methods still have room to be improved for PCB surface defect detection. This paper proposes an advanced model, MDUA-YOLO, based on YOLOv5 to increase the detection accuracy of defects on PCB surface. Firstly, we introduce the C3 Mobile Vision Transformer (C3MobileViT) module in the backbone of YOLOv5, improving the feature extraction capability of the model. Secondly, the Deformable Convolutional-Receptive Field Block (DC-RFB) module is incorporated into the neck of YOLOv5 to dynamically expands the receptive field and more accurately capture the location information of small defects. Additionally, we design the Union Attention Block (UAB) module in the neck of YOLOv5 to optimize the fusion of low-level and high-level feature maps. Finally, an extra prediction head and new feature fusion layer are also added to enhance the ability of the model to detect small defects. On the benchmark PKU-PCB and DeepPCB datasets, numerous experimental results show that the MDUA-YOLO surpasses other comparison state-of-the-art models and meets the real-time detection requirement of industrial environment.