EFFECTS OF CNN MODELS AND DIFFIRENT DIGITAL IMAGE RESOLUTION ON ACCURACY OF RUST CONDITION RATING OF WEATHERING STEEL

DOI 3 Citations
  • TAI Masayuki
    琉球大学工学部
  • SEKIYA Hidehiko
    東京都市大学建築都市デザイン学部 理化学研究所革新知能統合研究センターインフラ管理ロボット技術チーム
  • OKATANI Takayuki
    東北大学大学院情報科学研究科 理化学研究所革新知能統合研究センターインフラ管理ロボット技術チーム
  • NAKAMURA Shozo
    長崎大学大学院工学研究科
  • SHIMIZU Takashi
    建設技術研究所技術本部

Bibliographic Information

Other Title
  • 耐候性鋼板のさび外観評点識別精度に及ぼすCNNモデルと画像サイズの影響

Description

<p>The inspection and diagnosis of weathering steel bridges is based on the deterioration of anti-corrosion function, which is evaluated with the corrosion condition rating by observation. However, in order to perform an accurate evaluation by therating, inspectors need to have necessary to have adequate experience. Due to the recent shortage of human resources and inspection costs, it is needed to establish a simple and accurate evaluation method. In this study, the identification of rust condition has been investigated by using digital images of surface rust condition in weathering steel bridges and existing CNN models. Inaddition, the effect of digital image resolution on the identification accuracy has been considered. As a result, the accuracy isrelatively high in the case of VGG19 and SEnet. The larger input image size gives the better accuracy. Furthermore, the accuracy tends to decrease when the resolution of the images used for training and validation is different.</p>

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Details 詳細情報について

  • CRID
    1390290088581364736
  • NII Article ID
    130008118372
  • DOI
    10.11532/jsceiii.2.j2_378
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • Crossref
  • Abstract License Flag
    Disallowed

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