Surface defect detection is a challenging task in industrial manufacture. Recent methods using supervised learning need a large-scale dataset to achieve precise detection. However, the time-consuming and the difficulty of data acquisition make it difficult to build a large-scale dataset. This article proposes a domain adaptive network, called multiscale adversarial and weighted gradient domain adaptive network (MWDAN) for data scarcity surface defect detection. By MWDAN, the detection model trained from a small-scale dataset can gain the knowledge of transfer from another large-scale dataset, that is to say, even for a training dataset that is difficult to collect huge amounts of data, a good defect detection model can also be constructed, aided by another dataset that is relatively easy to acquire. The MWDAN is constructed in two levels. In the image level, a multiscale domain feature adaptation approach is proposed to solve the domain shift between the source domain and the target domain. In the instance level, a piecewise weighted gradient reversal layer (PWGRL) is designed to balance the weight of the backpropagation gradient for the hard- and easy-confused samples in domain classification and force confusion. Then, the PWGRL can reduce the local instance difference to further promote domain consistency. The experiments on mental surface defect detection show encourage results by the proposed MWDAN method.

中文翻译：

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用于数据稀缺表面缺陷检测的多尺度对抗和加权梯度域自适应网络


表面缺陷检测是工业制造中的一项具有挑战性的任务。最近使用监督学习的方法需要大规模数据集来实现精确检测。然而，数据获取的耗时和难度使得构建大规模数据集变得困难。本文提出了一种域自适应网络，称为多尺度对抗和加权梯度域自适应网络（MWDAN），用于数据稀缺表面缺陷检测。通过MWDAN，从小规模数据集训练的检测模型可以获得来自另一个大规模数据集的迁移知识，也就是说，即使对于难以收集大量数据的训练数据集，也能得到良好的缺陷检测还可以在另一个相对容易获取的数据集的帮助下构建模型。 MWDAN 分为两个层次。在图像级别，提出了一种多尺度域特征自适应方法来解决源域和目标域之间的域转移。在实例级别，设计了分段加权梯度反转层（PWGRL）来平衡域分类和强制混淆中难混淆和易混淆样本的反向传播梯度的权重。然后，PWGRL可以减少本地实例差异，进一步促进域一致性。心理表面缺陷检测的实验显示了所提出的 MWDAN 方法的令人鼓舞的结果。