FaNet: Feature-aware network for few shot classification of strip steel surface defects

Accurate classification of surface defects is one of the most important factors in achieving quality inspection for strip steel. Most existing methods are based on fully-supervised learning, which requires a large number of labeled training data samples. In the manufacturing process, collecting defective samples is time-consuming and laborious. So it is very difficult to train a fully supervised model based on few labeled samples. In this paper, we propose a feature-aware network (FaNet) for a few shot defect classification, which can effectively distinguish new classes with a small number of labeled samples. In our proposed FaNet, we use ResNet12 as our baseline. The feature-attention convolution module (FAC) is applied to extract the comprehensive feature information from the base classes, as well as to fuse semantic information by capturing the long-range feature relationships between the upper and lower layers. Meanwhile, during the test phase, an online feature-enhance integration module (FEI) is adopted to average the noise from the support set and query set defect images, further enhancing image features among the different tasks. In addition, we construct a large-scale strip steel surface defects few shot classification dataset (FSC-20) with 20 different types. Experimental results show that the proposed method achieves the best performance compared to state-of-the-art methods for the 5-way 1-shot and 5-way 5-shot tasks. The dataset and code are available at: https://github.com/VDT-2048/FSC-20.

Automated summary

This paper proposes a feature-aware network (FaNet) for few shot defect classification, which can effectively distinguish new classes with a small number of labeled samples. In FaNet, ResNet12 is used as the baseline, and the feature-attention convolution module (FAC) is applied to extract comprehensive feature information from the base classes. An online feature-enhance integration module (FEI) is adopted during the test phase to average the noise from defect images, further enhancing image features among different tasks. In addition, a large-scale strip steel surface defects few shot classification dataset (FSC-20) with 20 different types is constructed. Experimental results show that the proposed method achieves the best performance compared to state-of-the-art methods for 5-way 1-shot and 5-way 5-shot tasks. The dataset and code are available at: https://github.com/VDT-2048/FSC-20.