Real-time pothole detection with onboard sensors and camera on vehicles
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912710169460736 |
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| author | Muthuselvam, Aswath S, Jeevak Raj K, Mohanaprasad |
| author_facet | Muthuselvam, Aswath S, Jeevak Raj K, Mohanaprasad |
| contents | Road conditions play an important role in our everyday commute. With the proliferating number of vehicles on the road each year, it has become necessary to access the road conditions very frequently, this would ensure that the traffic also flows smoothly. Even the smallest crack in the road could be easily be chipped into a large pothole due to changing surface temperatures of the road and from the force of vehicles riding over it. In this paper, we have addressed how we could better identify these potholes in realtime with the help of onboard sensors in vehicles so that the data could be useful for analysis and better management of potholes on a large scale. For the implementation, we used an SVM classifier to detect potholes, we achieved 98.1% accuracy based on data collected from a local road for about 2 km which had 26 potholes distributed along the road. Code is available at: https://github.com/aswathselvam/Potholes |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time pothole detection with onboard sensors and camera on vehicles Muthuselvam, Aswath S, Jeevak Raj K, Mohanaprasad Computer Vision and Pattern Recognition Road conditions play an important role in our everyday commute. With the proliferating number of vehicles on the road each year, it has become necessary to access the road conditions very frequently, this would ensure that the traffic also flows smoothly. Even the smallest crack in the road could be easily be chipped into a large pothole due to changing surface temperatures of the road and from the force of vehicles riding over it. In this paper, we have addressed how we could better identify these potholes in realtime with the help of onboard sensors in vehicles so that the data could be useful for analysis and better management of potholes on a large scale. For the implementation, we used an SVM classifier to detect potholes, we achieved 98.1% accuracy based on data collected from a local road for about 2 km which had 26 potholes distributed along the road. Code is available at: https://github.com/aswathselvam/Potholes |
| title | Real-time pothole detection with onboard sensors and camera on vehicles |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.11643 |