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• W3200966602 endingPage "103935" @default.
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• W3200966602 abstract "• The automatic monitoring of road conditions for multiple countries is addressed. • Deep Learning models are trained for detecting road damages in India, Japan, and Czech. • Recommendations are provided for reusing the data and models released by any country. • A large-scale road damage dataset comprising 26,620 annotated road images is proposed. • The Global Road Damage Detection Challenge’2020 utilizes a part of the proposed data. Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses usability of Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26,620 images collected from multiple countries (India, Japan, and the Czech Republic) using smartphones. Thirdly, it proposes models capable of detecting and classifying road damages in more than one country. Lastly, the study provides recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. A part of the proposed dataset was utilized for Global Road Damage Detection Challenge’2020 and can be accessed at ( https://github.com/sekilab/RoadDamageDetector/ )." @default.
• W3200966602 created "2021-09-27" @default.
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• W3200966602 date "2021-12-01" @default.
• W3200966602 modified "2023-10-17" @default.
• W3200966602 title "Deep learning-based road damage detection and classification for multiple countries" @default.
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• W3200966602 doi "https://doi.org/10.1016/j.autcon.2021.103935" @default.
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