RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection
Assessment of on-time road condition is crucial for ensuring the safety of the motorist. One of the recent approaches to detecting road potholes is to analyze images captured from an unmanned aerial vehicle (UAV). Although the traditional deep learning model could perform accurate detection durin...
| Main Author: | |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115933/ http://psasir.upm.edu.my/id/eprint/115933/1/115933.pdf |
| _version_ | 1848866893369180160 |
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| author | Abd Rahman, Mohd Amiruddin |
| author_facet | Abd Rahman, Mohd Amiruddin |
| author_sort | Abd Rahman, Mohd Amiruddin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Assessment of on-time road condition is crucial for ensuring the safety of the motorist. One
of the recent approaches to detecting road potholes is to analyze images captured from an unmanned
aerial vehicle (UAV). Although the traditional deep learning model could perform accurate detection
during offline analysis, there is still a limitation of the available algorithms that could perform real-time
evaluation. Therefore, this study proposes a lightweight transformer algorithm, the real-time detection
transformer (RT-DETR), for online evaluation of road pothole images. The models were tested in practical
deployment scenarios and compared with several other object detection models, such as Faster RCNNSqueezeNet,
YOLOv8x, YOLOv9e, YOLOv10x, and YOLO11x. The results show that the RT-DETRPothole
outperformed all other models in detection accuracy, achieving the highest mAP0.50 (0.834) and
mAP0.50-0.95 (0.565), along with a high F1-Score (0.809), indicating superior precision and recall, and at
the same time it could maintain low inference time. Overall, RT-DETR-Pothole is the most suitable model
for real-time pothole detection, especially for detecting smaller, less visible potholes, with a resonable
inference time for pavement engineering applications. |
| first_indexed | 2025-11-15T14:27:50Z |
| format | Conference or Workshop Item |
| id | upm-115933 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:27:50Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1159332025-03-19T07:48:38Z http://psasir.upm.edu.my/id/eprint/115933/ RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection Abd Rahman, Mohd Amiruddin Assessment of on-time road condition is crucial for ensuring the safety of the motorist. One of the recent approaches to detecting road potholes is to analyze images captured from an unmanned aerial vehicle (UAV). Although the traditional deep learning model could perform accurate detection during offline analysis, there is still a limitation of the available algorithms that could perform real-time evaluation. Therefore, this study proposes a lightweight transformer algorithm, the real-time detection transformer (RT-DETR), for online evaluation of road pothole images. The models were tested in practical deployment scenarios and compared with several other object detection models, such as Faster RCNNSqueezeNet, YOLOv8x, YOLOv9e, YOLOv10x, and YOLO11x. The results show that the RT-DETRPothole outperformed all other models in detection accuracy, achieving the highest mAP0.50 (0.834) and mAP0.50-0.95 (0.565), along with a high F1-Score (0.809), indicating superior precision and recall, and at the same time it could maintain low inference time. Overall, RT-DETR-Pothole is the most suitable model for real-time pothole detection, especially for detecting smaller, less visible potholes, with a resonable inference time for pavement engineering applications. 2025 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/115933/1/115933.pdf Abd Rahman, Mohd Amiruddin (2025) RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection. In: 2025 9th International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2025), 17-21 Feb. 2025, Sapporo, Japan. (pp. 1-6). (Submitted) |
| spellingShingle | Abd Rahman, Mohd Amiruddin RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title | RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title_full | RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title_fullStr | RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title_full_unstemmed | RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title_short | RT-DETR-Pothole: lightweight real-time detection transformers for improved road pothole detection |
| title_sort | rt-detr-pothole: lightweight real-time detection transformers for improved road pothole detection |
| url | http://psasir.upm.edu.my/id/eprint/115933/ http://psasir.upm.edu.my/id/eprint/115933/1/115933.pdf |