A real-time pothole detection based on deep learning approach
Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and u...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/96017/1/SuhailaIsaak2021_ARealTimePotholeDetection.pdf |
| _version_ | 1796866137827311616 |
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| author | Yik, Y. K. Alias, N. E. Yusof, Y. Isaak, S. |
| author_facet | Yik, Y. K. Alias, N. E. Yusof, Y. Isaak, S. |
| author_sort | Yik, Y. K. |
| collection | ePrints |
| description | Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and uneven roads. Most news reports of road accidents are also caused by potholes that are almost 10-30 cm deep, coupled with heavy rainfall that reduces visibility among drivers, significant damage to the suspension system to the vehicle or unnecessary traffic congestion. In this paper, deep learning detection with YOLOv3 algorithm is proposed apart from researches ranging from accelerometer detection, image processing or machine learning based detection as it is easier to develop and provide more accurate results. After pothole has been detected in real-time webcam, the location will be logged and displayed using Google Maps API for visualization. a total of 330 sets of data were sampled for the implementation of the pothole detection training model. As the results, the model provided 65.05 mAP and 0.9 % precision rate and 0.41 recall rate. The limitation of YOLOv3 algorithm detection can be improve further using GPU with higher specification performances and can sample 1000 to 10,000 datasets. The proposed algorithm provides acceptably high precision and efficient pothole monitoring solution under different scenarios for the users and may benefit the public and the government to monitor pothole in real-time. |
| first_indexed | 2024-03-05T21:07:41Z |
| format | Conference or Workshop Item |
| id | utm.eprints-96017 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:07:41Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | utm.eprints-960172022-07-01T08:31:47Z http://eprints.utm.my/96017/ A real-time pothole detection based on deep learning approach Yik, Y. K. Alias, N. E. Yusof, Y. Isaak, S. QA75 Electronic computers. Computer science Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and uneven roads. Most news reports of road accidents are also caused by potholes that are almost 10-30 cm deep, coupled with heavy rainfall that reduces visibility among drivers, significant damage to the suspension system to the vehicle or unnecessary traffic congestion. In this paper, deep learning detection with YOLOv3 algorithm is proposed apart from researches ranging from accelerometer detection, image processing or machine learning based detection as it is easier to develop and provide more accurate results. After pothole has been detected in real-time webcam, the location will be logged and displayed using Google Maps API for visualization. a total of 330 sets of data were sampled for the implementation of the pothole detection training model. As the results, the model provided 65.05 mAP and 0.9 % precision rate and 0.41 recall rate. The limitation of YOLOv3 algorithm detection can be improve further using GPU with higher specification performances and can sample 1000 to 10,000 datasets. The proposed algorithm provides acceptably high precision and efficient pothole monitoring solution under different scenarios for the users and may benefit the public and the government to monitor pothole in real-time. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/96017/1/SuhailaIsaak2021_ARealTimePotholeDetection.pdf Yik, Y. K. and Alias, N. E. and Yusof, Y. and Isaak, S. (2021) A real-time pothole detection based on deep learning approach. In: 2020 International Symposium on Automation, Information and Computing, ISAIC 2020, 2 December 2020 - 4 December 2020, Beijing, Virtual. http://dx.doi.org/10.1088/1742-6596/1828/1/012001 |
| spellingShingle | QA75 Electronic computers. Computer science Yik, Y. K. Alias, N. E. Yusof, Y. Isaak, S. A real-time pothole detection based on deep learning approach |
| title | A real-time pothole detection based on deep learning approach |
| title_full | A real-time pothole detection based on deep learning approach |
| title_fullStr | A real-time pothole detection based on deep learning approach |
| title_full_unstemmed | A real-time pothole detection based on deep learning approach |
| title_short | A real-time pothole detection based on deep learning approach |
| title_sort | real time pothole detection based on deep learning approach |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/96017/1/SuhailaIsaak2021_ARealTimePotholeDetection.pdf |
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