基于改进MobileNetV3的色环电阻识别
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国家自然科学基金(NSFC62076209)


Recognition of Color-ring Resistors Based on Improved MobileNetV3
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    摘要:

    针对现有色环电阻识别方法中鲁棒性差、准确率低和运行速度慢等问题, 在MobileNetV3网络的基础上提出了一种轻量级的色环电阻图像识别算法. 首先在自建的色环电阻数据集上进行数据增强以增加样本数量, 提高模型鲁棒性. 然后在瓶颈结构中使用CBAM注意力模块, 增加模型在空间和通道上对特征的细化能力以提高模型准确率. 接着优化分类层, 删掉冗余的升维操作, 在提高准确率的同时减少参数量, 提高模型运算速度. 最后分别针对特征图大小和通道数不相等时添加跳跃连接, 提高模型在深层网络中的特征提取能力, 进一步提高模型准确率. 实验结果表明, 该模型在自建数据集上的识别准确率达到了98%, 可快速准确的对色环电阻进行识别. 该模型能够为电阻自动化识别提供新的技术参考.

    Abstract:

    To address the poor robustness, low accuracy, and slow operation of existing color-ring resistor recognition methods, this study proposes a lightweight image recognition algorithm for color-ring resistors based on the MobileNetV3 network. Firstly, data augmentation is conducted on a self-built data set to increase the sample size and improve the robustness of the model. Secondly, a convolutional?block?attention?module (CBAM) attention module is utilized in the bottleneck structure, which can enhance the ability of the model to refine features in space and channels for accuracy improvement. Thirdly, the classifier is optimized by removing the redundant dimension-increasing operations, which can reduce the number of parameters while improving accuracy and thereby speed up the operation of the model. Finally, a skip connection is embedded in the network in response to unequal feature image sizes and channel numbers. This makes the model able to extract more features from deep networks and improves accuracy. The experimental results show that the model can recognize color-ring resistors quickly and accurately, with its accuracy reaching 98% on the self-built data set. The model can provide a new technical reference for the automatic recognition of resistors.

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易子娟,贾渊.基于改进MobileNetV3的色环电阻识别.计算机系统应用,2023,32(4):361-367

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  • 收稿日期:2022-08-31
  • 最后修改日期:2022-09-30
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  • 在线发布日期: 2023-01-06
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