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Detection of defective part of inside manhole using deep learning for automation of inspection.
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- KATSUMURA Reon
- Nippon Telegraph and Telephone East Corporation
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- UMEDA Takayuki
- NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation
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- ANDOU Shingo
- NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation
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- SHIMAMURA Jun
- NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation
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- WADA Masaki
- Nippon Telegraph and Telephone East Corporation
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- SHIMABARA Hiroki
- Nippon Telegraph and Telephone East Corporation
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- AIHARA Takaaki
- Nippon Telegraph and Telephone East Corporation
Bibliographic Information
- Other Title
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- マンホール内部点検自動化のための深層学習を用いた不良箇所検出手法の検討
Description
<p>Currently, we inspect annually about 30 thousand manholes within NTT East’s jurisdiction. We take pictures of inside manhole using 360-degree camera on-site. The repair judgement of manhole is carried out visually by many people at the centralized inspection center. Using Convolutional Neural Network, which has been successful in the field of image recognition, is expected to reduce work amount of the visual check with automation of the repair judgment for manhole inspection photograph. In this study, we automate detection of defective part of inside manhole using Mask-RCNN. And we verified the detection accuracy.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 4L2GS1302-4L2GS1302, 2020
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390566775143058560
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- NII Article ID
- 130007857326
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed