Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning

Tomoya Fujii, Rie Jinki, Yuukou Horita*

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

3 被引用数 (Scopus)

抄録

SUMMARY The social infrastructure, including roads and bridges built during period of rapid economic growth in Japan, is now aging, and there is a need to strategically maintain and renew the social infrastructure that is aging. On the other hand, road maintenance in rural areas is facing serious problems such as reduced budgets for maintenance and a shortage of engineers due to the declining birthrate and aging population. Therefore, it is difficult to visually inspect all roads in rural areas by maintenance engineers, and a system to automatically detect road damage is required. This paper reports practical improvements to the road damage model using YOLOv5, an object detection model capable of real-time operation, focusing on road image features.

本文言語英語
ページ(範囲)1216-1219
ページ数4
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E106.A
9
DOI
出版ステータス出版済み - 2023/09/01

ASJC Scopus 主題領域

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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