# TJU-DHD: A Diverse High-Resolution Dataset for Object Detection

arXiv: https://arxiv.org/abs/2011.09170 · HTML: https://mlsanity.com/paper/2011.09170/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 30 of 31)

- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [SODFormer: Streaming Object Detection with Transformer Using Events and Frames](https://mlsanity.com/paper/2308.04047/) (2023)
- [Image-GS: Content-Adaptive Image Representation via 2D Gaussians](https://mlsanity.com/paper/2407.01866/) (2024)
- [Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines](https://mlsanity.com/paper/2406.14482/) (2024)
- [Weakly Aligned Feature Fusion for Multimodal Object Detection](https://mlsanity.com/paper/2204.09848/) (2022)
- [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://mlsanity.com/paper/2506.17455/) (2025)
- [CE-FPN: Enhancing Channel Information for Object Detection](https://mlsanity.com/paper/2103.10643/) (2021)
- [Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey](https://mlsanity.com/paper/2401.07801/) (2024)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [E-Scooter Rider Detection and Classification in Dense Urban Environments](https://mlsanity.com/paper/2205.10184/) (2022)
- [Task-Balanced Distillation for Object Detection](https://mlsanity.com/paper/2208.03006/) (2022)
- [FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs](https://mlsanity.com/paper/2301.06719/) (2023)
- [TDIOT: Target-driven Inference for Deep Video Object Tracking](https://mlsanity.com/paper/2103.11017/) (2021)
- [Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance](https://mlsanity.com/paper/2209.08237/) (2022)
- [HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI](https://mlsanity.com/paper/2408.03956/) (2024)
- [PhysLab: A Benchmark Dataset for Multi-Granularity Visual Parsing of Physics Experiments](https://mlsanity.com/paper/2506.06631/) (2025)
- [Real-time Traffic Object Detection for Autonomous Driving](https://mlsanity.com/paper/2402.00128/) (2024)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)
- [Pedestrian Detection by Exemplar-Guided Contrastive Learning](https://mlsanity.com/paper/2111.08974/) (2021)
- [The Impact of Partial Occlusion on Pedestrian Detectability](https://mlsanity.com/paper/2205.04812/) (2022)
- [NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles](https://mlsanity.com/paper/2302.09429/) (2023)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)
- [An Objective Method for Pedestrian Occlusion Level Classification](https://mlsanity.com/paper/2205.05412/) (2022)
- [ViM: Vision Middleware for Unified Downstream Transferring](https://mlsanity.com/paper/2303.06911/) (2023)
- [ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection](https://mlsanity.com/paper/2508.01253/) (2025)
- [Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models](https://mlsanity.com/paper/2509.02161/) (2025)
- [Resource Efficient Perception for Vision Systems](https://mlsanity.com/paper/2405.07166/) (2024)
- [From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety](https://mlsanity.com/paper/2510.03314/) (2025)

## Abstract (arXiv)

Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems. Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on the specific scenarios. Moreover, the popular datasets (e.g., KITTI and Citypersons) collected from the specific scenarios are limited in the number of images and instances, the resolution, and the diversity. To attempt to solve the problem, we build a diverse high-resolution dataset (called TJU-DHD). The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labeled objects in total with a large variance in scale and appearance. Meanwhile, the dataset has a rich diversity in season variance, illumination variance, and weather variance. In addition, a new diverse pedestrian dataset is further built. With the four different detectors (i.e., the one-stage RetinaNet, anchor-free FCOS, two-stage FPN, and Cascade R-CNN), experiments about object detection and pedestrian detection are conducted. We hope that the newly built dataset can help promote the research on object detection and pedestrian detection in these two scenes. The dataset is available at https://github.com/tjubiit/TJU-DHD.