dc.contributor.author |
Bello-Salau, H
|
|
dc.contributor.author |
Onumanyi, Adeiza J
|
|
dc.contributor.author |
Adebiyi, RF
|
|
dc.contributor.author |
Adekale, AD
|
|
dc.contributor.author |
Bello, RS
|
|
dc.contributor.author |
Ajayi, O
|
|
dc.date.accessioned |
2024-02-15T06:50:14Z |
|
dc.date.available |
2024-02-15T06:50:14Z |
|
dc.date.issued |
2023-10 |
|
dc.identifier.citation |
Bello-Salau, H., Onumanyi, A.J., Adebiyi, R., Adekale, A., Bello, R. & Ajayi, O. 2023. A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations. <i>Engineering Proceedings, 56(1).</i> http://hdl.handle.net/10204/13603 |
en_ZA |
dc.identifier.issn |
2673-4591 |
|
dc.identifier.uri |
https://doi.org/10.3390/ASEC2023-15519
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/13603
|
|
dc.description.abstract |
Road infrastructure is essential to national security and growth. Potholes on the road surface causes 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. Analysis shows that deep learning computer vision-based algorithms are 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 potholes detection. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/2673-4591/56/1/301 |
en_US |
dc.source |
Engineering Proceedings, 56(1) |
en_US |
dc.subject |
Computer vision |
en_US |
dc.subject |
Pothole detection |
en_US |
dc.subject |
Deep learning |
en_US |
dc.subject |
LiDAR |
en_US |
dc.subject |
Road infrastructure |
en_US |
dc.title |
A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
4 |
en_US |
dc.description.note |
Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
Advanced Internet of Things |
en_US |
dc.identifier.apacitation |
Bello-Salau, H., Onumanyi, A. J., Adebiyi, R., Adekale, A., Bello, R., & Ajayi, O. (2023). A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations. <i>Engineering Proceedings, 56(1)</i>, http://hdl.handle.net/10204/13603 |
en_ZA |
dc.identifier.chicagocitation |
Bello-Salau, H, Adeiza J Onumanyi, RF Adebiyi, AD Adekale, RS Bello, and O Ajayi "A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations." <i>Engineering Proceedings, 56(1)</i> (2023) http://hdl.handle.net/10204/13603 |
en_ZA |
dc.identifier.vancouvercitation |
Bello-Salau H, Onumanyi AJ, Adebiyi R, Adekale A, Bello R, Ajayi O. A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations. Engineering Proceedings, 56(1). 2023; http://hdl.handle.net/10204/13603. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Bello-Salau, H
AU - Onumanyi, Adeiza J
AU - Adebiyi, RF
AU - Adekale, AD
AU - Bello, RS
AU - Ajayi, O
AB - Road infrastructure is essential to national security and growth. Potholes on the road surface causes 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. Analysis shows that deep learning computer vision-based algorithms are 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 potholes detection.
DA - 2023-10
DB - ResearchSpace
DP - CSIR
J1 - Engineering Proceedings, 56(1)
KW - Computer vision
KW - Pothole detection
KW - Deep learning
KW - LiDAR
KW - Road infrastructure
LK - https://researchspace.csir.co.za
PY - 2023
SM - 2673-4591
T1 - A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations
TI - A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations
UR - http://hdl.handle.net/10204/13603
ER -
|
en_ZA |
dc.identifier.worklist |
27242 |
en_US |