The automatic detection of defects is an essential part of the printed circuit board (PCB) production process. In recent years, while great progress has been made in the detection of PCB defects, there are still various problems in traditional defect detection methods, for example, over-reliance on the perfect template, difficult to achieve precise image registration, and highly vulnerable to environmental factors such as light, noise, and reflectivity. We propose a fast defect detection network. On one hand, this algorithm solved the problems of traditional methods. On the other hand, this algorithm solved the problems of large model size and poor real-time of existing deep learning methods. First of all, the k-means clustering algorithm is used to obtain more reasonable anchors boxes; second, an improved MobileNetV2 is used as the backbone network; after the feature extraction network, the spatial pyramid pooling (SPP) structure is introduced to increase the receptive field of the image; then, we use complete intersection over union to optimize the loss function; finally, we build an enhanced feature extraction network based on the feature pyramid network for multi-scale feature fusion. The experimental results show that this method has small model size, good real-time, and good portability, which is suitable for practical production.

中文翻译：

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基于MobileNet-Yolo-Fast的印制电路板缺陷检测

缺陷的自动检测是印刷电路板 (PCB) 生产过程的重要组成部分。近年来，虽然PCB缺陷检测取得了很大进展，但传统的缺陷检测方法仍然存在着各种问题，例如过度依赖完美的模板、难以实现精确的图像配准、极易被环境因素，如光线、噪音和反射率。我们提出了一个快速缺陷检测网络。一方面，该算法解决了传统方法的问题。另一方面，该算法解决了现有深度学习方法模型规模大、实时性差的问题。首先，使用k-means聚类算法得到更合理的anchors box；第二，使用改进的 MobileNetV2 作为骨干网；在特征提取网络之后，引入空间金字塔池化（SPP）结构来增加图像的感受野；然后，我们使用完整的交集来优化损失函数；最后，我们构建了一个基于特征金字塔网络的增强特征提取网络，用于多尺度特征融合。实验结果表明，该方法模型尺寸小、实时性好、可移植性好，适合实际生产。我们基于特征金字塔网络构建了一个增强的特征提取网络，用于多尺度特征融合。实验结果表明，该方法模型尺寸小、实时性好、可移植性好，适合实际生产。我们基于特征金字塔网络构建了一个增强的特征提取网络，用于多尺度特征融合。实验结果表明，该方法模型尺寸小、实时性好、可移植性好，适合实际生产。