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• W4313342368 abstract "Roadways have always been one of the most used modes of transportation, and their contribution to the nation’s economy is also huge. To meet the demands of the growing global population and an increase in urbanization, there has been an exponential rise in the number of vehicles plying on the roads as well as the length of the roads. With this increase in traffic, coupled with other issues like heavy rainfall, the material used for the construction of the road, etc., the condition of the roads deteriorates with cracks and potholes developing on them, which may lead to serious accidents. For effective maintenance of roads and to reduce the associated risks, these defects must be detected. With the advent of Deep Learning (DL) in the recent past and its applications in various sectors, we have comprehensively explored various approaches, particularly using DL in this study, along with the associated challenges in adopting such techniques and future opportunities in this domain. Based on our analysis, using object detection-based models turned out to outperform other approaches." @default.
• W4313342368 created "2023-01-06" @default.
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• W4313342368 date "2022-01-01" @default.
• W4313342368 modified "2023-10-17" @default.
• W4313342368 title "Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities" @default.
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• W4313342368 doi "https://doi.org/10.1007/978-3-031-21750-0_17" @default.
• W4313342368 hasPublicationYear "2022" @default.
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