Research on Surface Defect Detection of Aerostat Main Cable Based on Few-shot Metric Learning

In view of the difficulty in obtaining the surface defect samples of the main cable of the aerostat, a few-shot learning method was proposed to detect the surface defects of the main cable. Few-shot metric learning consists of feature encoder module and metric module. The feature encoder module was used to extract features from a common used image dataset by a pre-training convolutional neural network (CNN), and the metric module was used to measure the samples that were the most similar to the unknown class samples, so as to complete the classification of defect. The experimental results show that the defect detection accuracy of this method is 93.17%, which is greatly improved compared with traditional machine learning and deep learning methods. The fine tuning strategy can further improve the detection performance of cable surface defects by this method, and the accuracy can be improved from 93.17% to 93.85%. On the steel defect classification dataset NEU, the accuracy of this method can also be 91.22%.