Automatic detection of surface defects based on deep random chains

Defect detection is critical in production systems. The traditional methods are primarily manual, prohibiting its large-scale industrial application. The current deep learning methods usually require a large amount of data, which is challenging in some cases. This paper presents a novel deep learning method to detect a large variety of defects based on small datasets only. Specifically, the method is based on the deep random chain combined with the adaptive Faster R-CNN. The idea behind this method is to fuse both common and different types of infor-mation among candidate groups to each defect candidate, which thus improves the model's generalization for small sample datasets with a wide variety of defects. Indeed, the deep random chains focus on learning the relationship among the pixels inside each defect, while many features are added to each defect using Faster R-CNN. Several experiments on industrial products demonstrate the merit of the proposed method for small sample datasets with yet a wide variety of defects.

Automated summary

This paper presents a novel deep learning method that can detect a wide variety of defects based on small datasets. The method combines deep random chains with adaptive Faster R-CNN to improve the model's generalization for small sample datasets with diverse defects.