TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
Xiao, Xi Li, Zhengji Wang, Wentao Xie, Jiacheng Lin, Houjie Roy, Swalpa Kumar Wang, Tianyang Xu, Min
Xiao, Xi
Li, Zhengji
Wang, Wentao
Xie, Jiacheng
Lin, Houjie
Roy, Swalpa Kumar
Wang, Tianyang
Xu, Min
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Object detection has witnessed remarkable advancements over the past decade, largely driven by breakthroughs in deep learning and the proliferation of large-scale datasets. However, the domain of road damage detection remains relatively underexplored, despite its critical significance for applications such as infrastructure maintenance and road safety. This paper addresses this gap by introducing a novel top-down benchmark that offers a complementary perspective to existing datasets, specifically tailored for road damage detection. Our proposed Top-Down Road Damage Detection Dataset (TD-RD) includes three primary categories of road damage—cracks, potholes, and patches—captured from an top-down viewpoint. The dataset consists of 7,088 high-resolution images, encompassing 12,882 annotated instances of road damage. Additionally, we present a novel real-time object detection framework, TD-YOLOV10, designed to handle the unique challenges posed by the TD-RD dataset. Comparative studies with state-of-the-art models demonstrate competitive baseline results. By releasing TD-RD, we aim to accelerate research in this crucial area. A sample of the dataset will be made publicly available upon the paper’s acceptance.
Citation
X. Xiao et al., "TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection," ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10888616.
Source
Proceedings - IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2025
Conference
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Keywords
Subjects
Source
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Publisher
IEEE