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Distress Image Retrieval for Infrastructure Maintenance via Self-Trained Deep Metric Learning Using Experts' Knowledge
Title: | Distress Image Retrieval for Infrastructure Maintenance via Self-Trained Deep Metric Learning Using Experts' Knowledge |
Authors: | Ogawa, Naoki Browse this author | Maeda, Keisuke Browse this author | Ogawa, Takahiro Browse this author →KAKEN DB | Haseyama, Miki Browse this author →KAKEN DB |
Keywords: | Inspection | Maintenance engineering | Image retrieval | Training | Measurement | Task analysis | Training data | Distress image retrieval | self-trained approach | pseudo-label | deep metric learning |
Issue Date: | 5-May-2021 |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Journal Title: | IEEE Access |
Volume: | 9 |
Start Page: | 65234 |
End Page: | 65245 |
Publisher DOI: | 10.1109/ACCESS.2021.3074019 |
Abstract: | 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 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. |
Rights: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Type: | article |
URI: | http://hdl.handle.net/2115/81535 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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