Voltage-dependent resistors (VDRs) are important circuit-protection devices. Their performance is affected by packaging quality. To identify VDR packaging defects more accurately and efficiently, we have proposed a convolutional neural network (CNN)-based VDR appearance quality inspection method that includes four stages: image acquisition, data augmentation, neural architecture design, and CNN training and testing. In designing the neural architecture, we have proposed two VDR-oriented network blocks, which consist of a compressed subnet and a multiscale subnet. Then, a stacking-block-based neural architecture design (BlockNAD) strategy is employed to determine the number of blocks. The last block is connected to a classification layer composed of a global average pooling (GAP) layer and a full connection (FC) layer. Further, using a VDR dataset containing 8058 images, we compared the identification performances of the candidate networks with different structures on 12 categories of VDR defects by adopting a variety of indicators, such as the mean average precision (mAP) and average test time per sample. The experimental results of the proposed method demonstrate competitive results compared to the state-of-the-art methods in identifying VDR defects, with a mAP value of approximately 99.9% and an average test time per sample of approximately 3 ms.

中文翻译：

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使用基于堆叠块的卷积神经网络检测电压相关电阻器中的缺陷

电压相关电阻器 (VDR) 是重要的电路保护设备。它们的性能受包装质量的影响。为了更准确有效地识别 VDR 包装缺陷，我们提出了一种基于卷积神经网络 (CNN) 的 VDR 外观质量检测方法，包括四个阶段：图像采集、数据增强、神经架构设计和 CNN 训练和测试。在设计神经架构时，我们提出了两个面向 VDR 的网络块，它们由压缩子网和多尺度子网组成。然后，采用基于堆叠块的神经架构设计（BlockNAD）策略来确定块的数量。最后一个块连接到由全局平均池化 (GAP) 层和全连接 (FC) 层组成的分类层。更多，使用包含 8058 张图像的 VDR 数据集，我们通过采用多种指标，例如平均平均精度 (mAP) 和每个样本的平均测试时间，比较了不同结构的候选网络对 12 类 VDR 缺陷的识别性能。与识别 VDR 缺陷的最先进方法相比，所提出方法的实验结果证明了具有竞争力的结果，mAP 值约为 99.9%，每个样本的平均测试时间约为 3 毫秒。