关键词: 排水管道缺陷, 小样本学习, CCTV检测, 度量学习, 智能识别

Abstract: The inspection, evaluation and maintenance of urban underground drainage pipelines are the necessary means to ensure the safe of drainage pipeline system. Deep learning technology provides a new method for the automation and intelligence of underground drainage pipelines defects detection and recognition. However, the lack of sample set for some pipeline defect and the imbalance of samples among different defect types greatly affect the generalization ability and robustness of defect recognition models, resulting in the problems of false detection, missed detection, and low recognition accuracy of existing defect recognition models. To solve the above problems, this paper proposes a defect recognition method for few-shot defect based on metric learning. The method uses detailed features to represent the test images, and identifies the types of defects according to the similarity between the feature vectors of different defect detection images in the support set and the images in the query set. The experimental results show that the accuracy of the proposed method in identifying scarce pipeline defect types is about 65%, and can be used as a solution for intelligent recognition in the case of insufficient and unbalanced pipeline defect samples.

Key words: sewer defects, few-shot learning, CCTV inspection, metric learning, intelligent recognition