CLASSIFICATION OF CORROSION DETERIORATION ON WEATHERING STEEL BASED ON CNN

DOI
  • HASUIKE Rina
    山口大学大学院 創成科学研究科社会建設工学分野 岐阜大学 工学部社会基盤工学科
  • KINOSHITA Koji
    岐阜大学 工学部社会基盤工学科

Bibliographic Information

Other Title
  • 畳み込みニューラルネットワークを用いた耐候性鋼材腐食部劣化度判定の試み

Abstract

<p>This paper aims to develop an image processing for diagnosis of deterioration level of corroded weathering steel surface based on Convolutional Neural Network (CNN) analysis. The weathering steel corrosion test results, which had been obtained in differenent chloride environment, were used for this image processing. At first, the relationship between surface and gained weight or rust thickness were clarified to determine the deterioration level. Then, the CNN analysis was conducted for the dataset from each chloride environment and all chloride environments. As the result, there was no strong relationship between surface and gained weight or rust thickness. The accuracy of the CNN classifier created for each corrosion surface image obtained in each chloride environment is depending on the environment. Also, the accuracy of the CNN classifier using images obtained in all environments was low. But some deterioration level could be classified with high accuracy in the classification results for each deterioration level, therefore improvement may be expected. </p>

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Details 詳細情報について

  • CRID
    1390008613604250240
  • NII Article ID
    130008118400
  • DOI
    10.11532/jsceiii.2.j2_813
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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