An efficient network for surface defect detection

Abstract

Convolutional neural networks (CNN) have achieved promising performance in surface defect detection recently. Although many CNN-based methods have been proposed, most of them are limited by the few samples available for training, and the imbalance of positive and negative samples. Hence, their detection performance needs to be further improved. To this end, we propose a multi-scale cascade CNN called MobileNet-v2-dense to detect defects more efficiently. Specifically, the multi-scale cascade structure used in our network can help capture the weak defect semantics that may be lost in the deep network. Then, we propose a novel asymmetric loss function to further improve detection performance. Lastly, a two-stage augmentation method effectively enlarges the training dataset. Experimental results show that, compared to the state-of-the-art, the area under the receiver-operating characteristic curve (AUC-ROC) score of our method increased by 0.16.