Accurately classifying defects in inspection images is a crucial technology in the field of automatic inspection of transmission lines.To address the problem of overfitting and low accuracy due to the small number of defect category images in traditional deep learning techniques,we proposed a few-shot classification method for transmission line image component defects based on meta-metric learning.Firstly,we built a few-shot learning-based image classification network using convolutional layers based on deep residual structures that enhance feature representation capability.Then,a Pear-son similarity-based k-nearest neighbor algorithm and a local feature descriptor re-weighting mechanism were introduced in the metric module to improve the classification ability of the network.Finally,to validate the effectiveness of the pro-posed method,experiments were conducted to comparatively analyze this method and other few-shot classification methods based on meta metric learning after using a dataset composed of inspection images.The results indicate that the method proposed in this paper has significant advantages in classification performance.Meanwhile,the average accuracy of the algorithm in each defect category reaches 80.24%with only 15 test images per category.
关键词
小样本分类/元度量学习/皮尔森相似度/局部描述符重加权/输电线路图像
Key words
few-shot classification/meta-metric learning/Pearson similarity/local descriptor re-weighting/transmission line images
维普
万方数据