An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018-Reference-Cited by-同舟云学术

An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018

Published:2020-03-06 Issue: Volume: Page:
ISSN:
Container-title:2020 International Conference on Image Processing and Robotics (ICIP)
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Author:

Kamada Shin¹,Ichimura Takumi²,Iwasaki Takashi³

Affiliation:

1. Prefectural University of Hiroshima,Advanced Artificial Intelligence Project Research Center, Research Organization of Regional Oriented Studies,Minami-ku, Hiroshima,Japan,734-8558

2. Prefectural University of Hiroshima,Advanced Artificial Intelligence Project Research Center, Research Organization of Regional Oriented Studies, Faculty of Management and Information System,Minami-ku, Hiroshima,Japan,734-8558

3. Mitsui Consultants Co. Ltd.,MCC Laboratory, Infrastructure System Group,Shinagawa, Tokyo,Japan,141-0032

Funder

National Institute of Information and Communications Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9367308/9367328/09367339.pdf?arnumber=9367339

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1. Adaptive Structural Learning of Deep Belief Network and Its Application to Real Time Crack Detection of Concrete Structure Using Drone;Handbook on Artificial Intelligence-Empowered Applied Software Engineering;2022

2. Studying Cracks in Infrastructures Using Amalgamation of AlexNet and Moth Flame Optimizer;2021 International Conference on Decision Aid Sciences and Application (DASA);2021-12-07

3. Semantic Segmentation Models for Crack Detection: Using Shelled Unmanned Aerial Vehicle Imagery;2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM);2021-11-28