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

TL;DR

This paper introduces a new top-down dataset and real-time detection framework for road damage, aiming to advance infrastructure maintenance and safety applications with novel data and methods.

Contribution

It presents the TDRD dataset with top-down images of road damage and the TDYOLOV10 real-time detection framework, filling a gap in existing datasets and models.

Findings

TDRD dataset contains 7,088 images and 12,882 damage instances.

TDYOLOV10 achieves competitive detection performance.

The dataset and framework facilitate future research in road damage detection.

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 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.