Performance Evaluation and Consideration of Road Surface Damage Detection Model Using YOLO

DOI
  • FUJII Tomoya
    Graduate School of Sustainable Studies, University of Toyama
  • JINKI Rie
    School of Sustainable Design, University of Toyama
  • HORITA Yuukou
    Faculty of Sustainable Design, University of Toyama

Bibliographic Information

Other Title
  • YOLO を用いた路面損傷検出モデルの性能評価と考察

Abstract

The social infrastructure, including roads and bridges built during Japan's period of rapid economic growth, is rapidly deteriorating, and there is a need to strategically maintain and renew the social infrastructure that is aging all at once. On the other hand, in road maintenance and management in rural areas, it is not realistic to increase the number of road management patrol cars or the number of specialized engineers engaged in road maintenance and management, and the reduction of management budgets and the shortage of engineers due to the declining birthrate and aging population are serious problems. In addition, in rural areas, it is difficult to conduct all road inspections by visual inspection, which is performed by expert road maintenance technicians, and an inexpensive, high-precision system that can automatically detect road surface damage through image analysis or other means is required. In this study, we construct a road surface damage detection model using YOLOv5, a machine learning algorithm capable of real-time.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390861936166807808
  • DOI
    10.11371/wiieej.22.03.0_24
  • ISSN
    27589218
    02853957
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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