IEICE TRANSACTIONS on Fundamentals
Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.9 pp.1216-1219
- Publication Date
- 2023/09/01
- Publicized
- 2023/06/13
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022IML0003
- Type of Manuscript
- Special Section LETTER (Special Section on Image Media Quality)
- Category
- Image
Authors
Tomoya FUJII
University of Toyama
Rie JINKI
University of Toyama
Yuukou HORITA
University of Toyama
Keyword
Latest Issue
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Tomoya FUJII, Rie JINKI, Yuukou HORITA, "Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 9, pp. 1216-1219, September 2023, doi: 10.1587/transfun.2022IML0003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022IML0003/_p
Copy
@ARTICLE{e106-a_9_1216,
author={Tomoya FUJII, Rie JINKI, Yuukou HORITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning},
year={2023},
volume={E106-A},
number={9},
pages={1216-1219},
abstract={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.},
keywords={},
doi={10.1587/transfun.2022IML0003},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1216
EP - 1219
AU - Tomoya FUJII
AU - Rie JINKI
AU - Yuukou HORITA
PY - 2023
DO - 10.1587/transfun.2022IML0003
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2023
AB - 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.
ER -
English
