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

arXiv: https://arxiv.org/abs/2501.14302 · HTML: https://mlsanity.com/paper/2501.14302/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://mlsanity.com/paper/2106.00666/) (2021)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices](https://mlsanity.com/paper/2111.00902/) (2021)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2406.17697/) (2024)
- [Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching](https://mlsanity.com/paper/2405.03782/) (2024)
- [Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection](https://mlsanity.com/paper/2405.17905/) (2024)
- [Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2407.10694/) (2024)
- [Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design](https://mlsanity.com/paper/2403.03412/) (2024)

## Built on by (most cited first, 6 of 6)

- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation](https://mlsanity.com/paper/2506.23121/) (2025)
- [CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization](https://mlsanity.com/paper/2503.24182/) (2025)
- [PFedDST: Personalized Federated Learning with Decentralized Selection Training](https://mlsanity.com/paper/2502.07750/) (2025)
- [Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection](https://mlsanity.com/paper/2511.12410/) (2025)
- [E2CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction](https://mlsanity.com/paper/2502.12186/) (2025)

## Abstract (arXiv)

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.