The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
As infra-estruturas sociais, incluindo estradas e pontes construídas durante o período de rápido crescimento económico no Japão, estão agora a envelhecer e há uma necessidade de manter e renovar estrategicamente as infra-estruturas sociais que estão a envelhecer. Por outro lado, a manutenção de estradas nas zonas rurais enfrenta problemas graves, como orçamentos reduzidos para manutenção e escassez de engenheiros devido ao declínio da taxa de natalidade e ao envelhecimento da população. Portanto, é difícil inspecionar visualmente todas as estradas nas áreas rurais pelos engenheiros de manutenção, e é necessário um sistema para detectar automaticamente os danos nas estradas. Este artigo relata melhorias práticas no modelo de danos rodoviários usando YOLOv5, um modelo de detecção de objetos capaz de operar em tempo real, com foco em características de imagens rodoviárias.
Tomoya FUJII
University of Toyama
Rie JINKI
University of Toyama
Yuukou HORITA
University of Toyama
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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
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@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},}
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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 -