电动汽车 PCB 检测的进展:多尺度 CBAM、部分卷积和 NWD Loss 在 YOLOv5 中的应用

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC World Electric Vehicle Journal Pub Date : 2024-01-03 DOI:10.3390/wevj15010015
Hanlin Xu, Li Wang, Feng Chen
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引用次数: 0

摘要

在快速发展的电动汽车行业,电子系统的可靠性对于确保汽车的安全和性能至关重要。印刷电路板(PCB)作为这些系统的基石,需要高效准确的表面缺陷检测。传统的印刷电路板表面缺陷检测方法,如基本图像处理和人工检测,效率低下且容易出错,尤其是对于复杂、微小或不规则的缺陷。针对这一问题,本研究引入了一种基于 YOLOv5 网络结构的技术。通过整合卷积块注意模块(CBAM),该模型识别复杂和微小缺陷的能力得到了增强。此外,部分卷积(PConv)取代了传统的卷积,从而实现了更有效的空间特征提取并减少了冗余计算。在网络的最后阶段,实现了多尺度缺陷检测。此外,考虑到不同类别之间的关系,还引入了归一化瓦瑟斯坦距离(NWD)损失函数,从而有效解决了类别不平衡和多尺度缺陷检测问题。在公共印刷电路板数据集上进行的训练和验证表明,与传统方法相比,该模型的检测精度更高,误检率更低。实时监测结果证实了该模型能够准确检测出各种类型和尺寸的印刷电路板表面缺陷,满足了电动汽车生产线的实时检测需求,为电动汽车的可靠性提供了重要的技术支持。
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Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5
In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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