构建首个俯视视角道路损伤检测数据集，推动智能养护发展
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
- 提出俯视视角道路损伤数据集TDRD，涵盖裂缝、坑洼、修补三类损伤
- 包含7088张高分辨率图像和12882个标注实例，支持实时检测
- 设计TDYOLOV10框架，为道路损伤检测提供高效基准模型
过去十年中，目标检测在深度学习和大规模数据集推动下取得了显著进展。然而，道路损伤检测领域仍相对未被充分探索，尽管其对基础设施维护和道路安全至关重要。本文通过引入一个全新的自上而下基准，为现有数据集提供互补视角，专门针对道路损伤检测。我们提出的顶视道路损伤检测数据集（TDRD）包含三种主要类型的损伤：裂缝、坑洼和修补，均从俯视角度采集。该数据集共包含7,088张高分辨率图像，涵盖12,882个标注的损伤实例。此外，我们还提出一种新型实时目标检测框架TDYOLOV10，专为应对TDRD数据集的独特挑战而设计。与先进模型的对比研究表明其具备竞争力的基线性能。通过发布TDRD，我们旨在加速该关键领域的研究进展。论文被接受后，将公开部分数据集样本。
原文摘要 · Abstract (English)
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 under explored, 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 (TDRD) includes three primary categories of road damage cracks, potholes, and patches captured from a 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, TDYOLOV10, designed to handle the unique challenges posed by the TDRD dataset. Comparative studies with state of the art models demonstrate competitive baseline results. By releasing TDRD, we aim to accelerate research in this crucial area. A sample of the dataset will be made publicly available upon the paper's acceptance.
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