A Critical Appraisal of Various Implementation Approaches for Real-time Pothole Anomaly Detection: Toward Safer Roads in Developing Nations

Road infrastructure is essential to national security and growth. Potholes on the road surface cause accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real-time may address this challenge. These approaches can improve road safety and lower vehicle m...

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Published inEngineering proceedings Vol. 56; no. 1; p. 301
Main Authors Habeeb Bello-Salau, Adeiza James Onumanyi, Risikat Folashade Adebiyi, Abdulfatai Dare Adekale, Ridwan Bello-Salahuddeen, Ore-Ofe Ajayi
Format Journal Article
LanguageEnglish
Published MDPI AG 01.10.2023
Subjects
computer vision
deep learning
detection
LiDAR
potholes
road
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Summary:Road infrastructure is essential to national security and growth. Potholes on the road surface cause accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real-time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. An analysis shows that deep learning computer-vision-based algorithms are the most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. Meanwhile, the sensor-based solutions are cost-effective and can be utilized in developing nations for pothole detection.
ISSN:2673-4591
DOI:10.3390/ASEC2023-15519