TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection

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.