Journal
APPLIED SCIENCES-BASEL
Volume 10, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/app10176085
Keywords
convolution neural network; data augmentation; multi-scale cascade connection; surface defect detection
Categories
Funding
- National Key R&D Program of China [2018YFB0504900]
- National Natural Science Foundation of China [61871424]
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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.
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