Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance
Potholes are one of the most important issues in African road-networks. They pose a major threat to mobility and, with time, cause accelerated degradation of the underlying road infrastructure as well as extensive vehicle damage. To address the need for improved infrastructure management, an advance...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10015740/ |
| _version_ | 1797902232072486912 |
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| author | Darryn Anton Jordan Stephen Paine Amit Kumar Mishra Jan Pidanic |
| author_facet | Darryn Anton Jordan Stephen Paine Amit Kumar Mishra Jan Pidanic |
| author_sort | Darryn Anton Jordan |
| collection | DOAJ |
| description | Potholes are one of the most important issues in African road-networks. They pose a major threat to mobility and, with time, cause accelerated degradation of the underlying road infrastructure as well as extensive vehicle damage. To address the need for improved infrastructure management, an advanced data gathering solution is required. This paper presents one such solution. The pothole detection, classification and logging (PDCL) system is under active development by Sensorit (Pty) Ltd in collaboration with the University of Cape Town (UCT) Radar Remote Sensing Group (RRSG). This system represents the initial step in Sensorit’s modular approach to producing fully autonomous vehicles for African markets. In this paper, an overview of the PDCL system is presented and early results are shown. The efficacy of the system’s unique combination of active infrared stereo vision and mmWave frequency-modulated continuous-wave (FMCW) radar sensors is explored. Under various experimental conditions, range-Doppler maps (RDMs) produced by the radar were unable to provide meaningful pothole detections. In contrast, processed depth maps produced by the stereo vision system are demonstrated to successfully detect even shallow potholes. |
| first_indexed | 2024-04-10T09:14:27Z |
| format | Article |
| id | doaj.art-880a6f7aabe04ee9b665d722708ee553 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-880a6f7aabe04ee9b665d722708ee5532023-02-21T00:02:01ZengIEEEIEEE Access2169-35362023-01-01116010601710.1109/ACCESS.2023.323640110015740Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road MaintenanceDarryn Anton Jordan0https://orcid.org/0000-0003-4047-9733Stephen Paine1Amit Kumar Mishra2https://orcid.org/0000-0001-6631-1539Jan Pidanic3https://orcid.org/0000-0003-1948-3818Sensorit (Pty) Ltd, Cape Town, South AfricaDepartment of Electrical Engineering, University of Cape Town, Cape Town, South AfricaDepartment of Electrical Engineering, University of Cape Town, Cape Town, South AfricaDepartment of Electrical Engineering, University of Pardubice, Pardubice, Czech RepublicPotholes are one of the most important issues in African road-networks. They pose a major threat to mobility and, with time, cause accelerated degradation of the underlying road infrastructure as well as extensive vehicle damage. To address the need for improved infrastructure management, an advanced data gathering solution is required. This paper presents one such solution. The pothole detection, classification and logging (PDCL) system is under active development by Sensorit (Pty) Ltd in collaboration with the University of Cape Town (UCT) Radar Remote Sensing Group (RRSG). This system represents the initial step in Sensorit’s modular approach to producing fully autonomous vehicles for African markets. In this paper, an overview of the PDCL system is presented and early results are shown. The efficacy of the system’s unique combination of active infrared stereo vision and mmWave frequency-modulated continuous-wave (FMCW) radar sensors is explored. Under various experimental conditions, range-Doppler maps (RDMs) produced by the radar were unable to provide meaningful pothole detections. In contrast, processed depth maps produced by the stereo vision system are demonstrated to successfully detect even shallow potholes.https://ieeexplore.ieee.org/document/10015740/Machine learningradarFMCW radarroad maintainance |
| spellingShingle | Darryn Anton Jordan Stephen Paine Amit Kumar Mishra Jan Pidanic Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance IEEE Access Machine learning radar FMCW radar road maintainance |
| title | Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance |
| title_full | Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance |
| title_fullStr | Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance |
| title_full_unstemmed | Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance |
| title_short | Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance |
| title_sort | road to repair r2r an afrocentric sensor based solution to enhanced road maintenance |
| topic | Machine learning radar FMCW radar road maintainance |
| url | https://ieeexplore.ieee.org/document/10015740/ |
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