Multiscale Adversarial and Weighted Gradient Domain Adaptive Network for Data Scarcity Surface Defect Detection

Abstract

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.