論文

査読有り
2017年

A Feasibility Study on Crack Identification Utilizing Images Taken from Camera Mounted on a Mobile Robot

STRUCTURAL HEALTH MONITORING - FROM SENSING TO DIAGNOSIS AND PROGNOSIS
  • C. W. Kim
  • ,
  • K. C. Chang
  • ,
  • Y. Sasaka
  • ,
  • Y. Suzuki

188
開始ページ
48
終了ページ
55
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1016/j.proeng.2017.04.456
出版者・発行元
ELSEVIER SCIENCE BV

Many roadway and highway bridges have been suffering from aging and deterioration problems. To inspect those bridges more efficiently and accurately, the Japanese government launched a series of national projects aiming to develop various innovative robotic inspection systems to support conventional visual inspections. This study is devoted to developing the sensing modules compatible with the robotic inspection system. To preliminarily investigate the feasibility of the image-type sensing modules, a laboratory experiment was conducted, taking a commercially available digital camera and a digital video camera as the sensors and a model vehicle moving on rails as the robot. Two concrete blocks were placed at a certain distance away from the sensing system and serving as inspection targets. On still images taken by the camera, it was verified that the clear identification strongly depended on the short object distance, bright target surface, and quick shutter speed. Herein, the following condition presented a successful identification: 1-m object distance, 2300-lx illuminance, F2.8 aperture, 1/250-s shutter speed, 4608 3456 image resolution, and theoretical space resolution 0.09 mm/pixel. Longer object distance, faster moving speed, darker object surface and poorer theoretical space resolution would decrease the identification level. In videos taken by the digital video camera, it was verified that an object distance as short as 0.17 m could provide a high quality video from which the crack could be successfully identified. Those observations provided a useful basis for further development of the robot sensing system. (C) 2016 The Authors. Published by Elsevier Ltd.

リンク情報
DOI
https://doi.org/10.1016/j.proeng.2017.04.456
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000416999100007&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.proeng.2017.04.456
  • ISSN : 1877-7058
  • Web of Science ID : WOS:000416999100007

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