  Surface defect detection plays an increasingly important role in
manufacturing industry to guarantee the product quality. Many deep learning
methods have been widely used in surface defect detection tasks, and have been
proven to perform well in defects classification and location. However, deep
learning-based detection methods often require plenty of data for training,
which fail to apply to the real industrial scenarios since the distribution of
defect categories is often imbalanced. In other words, common defect classes
have many samples but rare defect classes have extremely few samples, and it is
difficult for these methods to well detect rare defect classes. To solve the
imbalanced distribution problem, in this paper we propose TL-SDD: a novel
Transfer Learning-based method for Surface Defect Detection. First, we adopt a
two-phase training scheme to transfer the knowledge from common defect classes
to rare defect classes. Second, we propose a novel Metric-based Surface Defect
Detection (M-SDD) model. We design three modules for this model: (1) feature
extraction module: containing feature fusion which combines high-level semantic
information with low-level structural information. (2) feature reweighting
module: transforming examples to a reweighting vector that indicates the
importance of features. (3) distance metric module: learning a metric space in
which defects are classified by computing distances to representations of each
category. Finally, we validate the performance of our proposed method on a real
dataset including surface defects of aluminum profiles. Compared to the
baseline methods, the performance of our proposed method has improved by up to
11.98% for rare defect classes.
