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...
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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 |
| Summary: | 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. |
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