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邓智超, 颜润明, 杨蕙同, 陈浩林, 赖锦祥, 雷亮. 基于改进残差网络的多视图焊点缺陷检测[J]. 焊接学报, 2022, 43(3): 56-62. DOI: 10.12073/j.hjxb.20210928004
引用本文: 邓智超, 颜润明, 杨蕙同, 陈浩林, 赖锦祥, 雷亮. 基于改进残差网络的多视图焊点缺陷检测[J]. 焊接学报, 2022, 43(3): 56-62. DOI: 10.12073/j.hjxb.20210928004
DENG Zhichao, YAN Runming, YANG Huitong, CHEN Haolin, LAI Jinxiang, LEI Liang. Multiview solder joint defect detection based on improved ResNet[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(3): 56-62. DOI: 10.12073/j.hjxb.20210928004
Citation: DENG Zhichao, YAN Runming, YANG Huitong, CHEN Haolin, LAI Jinxiang, LEI Liang. Multiview solder joint defect detection based on improved ResNet[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(3): 56-62. DOI: 10.12073/j.hjxb.20210928004

基于改进残差网络的多视图焊点缺陷检测

Multiview solder joint defect detection based on improved ResNet

  • 摘要: 基于卷积神经网络的单视图检测不能有效识别三维形状的缺陷目标,导致在实际应用中,往往是通过只检测某一最具代表性的视图或者依次检测每个面来实现低精度的检测要求,这带来了较大的时间成本和使用限制. 针对这一问题,提出了改进的残差网络(ResNet),并将其应用于三维形状的焊点缺陷检测. 该模型首先会一次性获取焊点的所有视图图像,再通过特征聚合和自适应学习模块,最终获得检测结果. 多视图焊点数据集来自高频电感元件,在所提出方法的识别精度达到了99.48%. 结果表明,改进的残差网络在同等网络层数的情况下有效提升了图像识别精度;对比单视图检测,多视图检测结构仅以较少的时间代价获得了较大的精度提升,能有效完成实际工业生产中的三维形状缺陷目标的检测任务.

     

    Abstract: The single view detection based on convolutional neural network cannot effectively identify 3D shape defect targets. As a result, in practical applications, the low-precision detection requirements are often achieved by detecting only one of the most representative views or detecting each face in turn, which brings large time cost and use restrictions. To solve this problem, this paper proposes an improved residual network and applies it to 3D shape solder joint defect detection. The model can firstly obtain all view images of solder joints at one time, and then get detection results through feature aggregation and adaptive learning module. The multi-view solder joint data set was obtained from high frequency inductor element, and the identification accuracy of the proposed method reached 99.48%. The results show that the improved residual network can effectively improve the image identification accuracy under the same number of network layers. Compared with the single view detection, the multi-view detection structure can achieve greater accuracy with less time cost, and can effectively complete the detection task of 3D shape defect targets in actual industrial production.

     

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