Distress Image Retrieval for Infrastructure Maintenance via Self-trained Deep Metric Learning Using Experts' Knowledge

Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
2021 IEEE Access  
Distress image retrieval for infrastructure maintenance via self-trained deep metric learning using experts' knowledge is proposed in this paper. Since engineers take multiple images of a single distress part for inspection of road structures, it is necessary to construct a similar distress image retrieval method considering the input of multiple images to support determination of the level of deterioration. Thus, the construction of an image retrieval method while selecting an effective input
more » ... an effective input from multiple images is described in this paper. The proposed method performs deep metric learning by using a small number of effective images labeled by experts' knowledge with information about their effectiveness and a large number of unlabeled images via a self-training approach. Specifically, an end-to-end learning approach that performs retraining of the model by assigning pseudo-labels to these unlabeled images according to the output confidence of the model is achieved. Thus, the proposed method can select an effective image from multiple images that are input at the retrieval as a query image. This is the main contribution of this paper. As a result, the proposed method realizes highly accurate retrieval of similar distress images considering the actual situation of inspection in which multiple images of a distress part are input. INDEX TERMS Distress image retrieval, self-trained approach, pseudo-label, deep metric learning.
doi:10.1109/access.2021.3074019 fatcat:arpi7u4dvneujcqhhcpeh4nxri