改进YOLOv8的PCB表面缺陷检测算法
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东北石油大学物理与电子工程学院

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TP391;TN41

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黑龙江省高等教育教学改革项目,海南省科技专项资助


Improved PCB surface defect detection algorithm for YOLOv8
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    摘要:

    针对目前PCB表面缺陷检测方法存在复杂度过高、误检、漏检等问题,提出了一种改进YOLOv8的轻量型检测算法。由于YOLOv8主干网络下采样后的特征图存在一定冗余,设计轻量级的多尺度混合卷积(MSMC),并结合C2f模块增强多尺度特征提取能力;在颈部网络中设计改进的双向特征金字塔结构(BiFPN),使用两个跨层连接得到更加丰富的语义信息;使用C2f-Faster模块减少特征融合过程中的运算量;引入CA注意力机制与WIoUv2损失函数,增强对PCB小目标缺陷的定位能力。实验结果表明,相较于YOLOv8n,改进后的算法在PCB数据集上检测精度提高了2.2%,模型参数量和计算量降低了36.7%和18.5%,分别为1.9M和6.6G。最终模型大小仅为3.8MB,为移动终端设备部署提供了新思路。

    Abstract:

    A lightweight detection algorithm based on improving YOLOv8 is proposed to address the issues of high complexity, false alarms, and missed detections in current PCB surface defect detection methods. Due to some redundancy in the feature maps of the YOLOv8 backbone network after downsampling, a lightweight multi-scale mixed convolution (MSMC) is designed. This is combined with the C2f module to enhance the capability of extracting features at different scales. Additionally, an improved Bidirectional Feature Pyramid Network (BiFPN) structure is designed in the neck network, using two cross-layer connections to obtain richer semantic information. The C2f-Faster module is employed to reduce computational complexity during the feature fusion process. Moreover, the introduction of the CA attention mechanism and the WIoUv2 loss function strengthens the ability to locate small defects on PCBs. The experimental results show that the improved algorithm compared to YOLOv8n improves the detection accuracy by 2.2% on the PCB dataset, while the number of model parameters and the computation volume are reduced by 36.7% and 18.5% to 1.9M and 6.6G. The final model size is only 3.8MB, providing a new approach for mobile terminal device deployment.

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  • 收稿日期:2024-04-09
  • 最后修改日期:2024-07-08
  • 录用日期:2024-07-09
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