# HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

arXiv: https://arxiv.org/abs/2204.03245 · HTML: https://mlsanity.com/paper/2204.03245/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning](https://mlsanity.com/paper/2003.02437/) (2020)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2001.11737/) (2020)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)

## Built on by (most cited first, 30 of 34)

- [EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain](https://mlsanity.com/paper/2401.16822/) (2024)
- [Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want](https://mlsanity.com/paper/2403.20271/) (2024)
- [EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues](https://mlsanity.com/paper/2412.15190/) (2024)
- [SynDrone -- Multi-modal UAV Dataset for Urban Scenarios](https://mlsanity.com/paper/2308.10491/) (2023)
- [RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning](https://mlsanity.com/paper/2507.20776/) (2025)
- [Caltech Aerial RGB-Thermal Dataset in the Wild](https://mlsanity.com/paper/2403.08997/) (2024)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [EarthGPT-X: A Spatial MLLM for Multi-level Multi-Source Remote Sensing Imagery Understanding with Visual Prompting](https://mlsanity.com/paper/2504.12795/) (2025)
- [UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection](https://mlsanity.com/paper/2406.04647/) (2024)
- [UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks](https://mlsanity.com/paper/2406.06230/) (2024)
- [SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems](https://mlsanity.com/paper/2504.15728/) (2025)
- [Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks](https://mlsanity.com/paper/2606.10819/) (2026)
- [Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking](https://mlsanity.com/paper/2412.20750/) (2024)
- [Self-Supervised Learning for Real-World Object Detection: a Survey](https://mlsanity.com/paper/2410.07442/) (2024)
- [ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11](https://mlsanity.com/paper/2502.14314/) (2025)
- [Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasets](https://mlsanity.com/paper/2507.06797/) (2025)
- [Task-Guided Prompting for Unified Remote Sensing Image Restoration](https://mlsanity.com/paper/2604.02742/) (2026)
- [Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation](https://mlsanity.com/paper/2303.05240/) (2023)
- [A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges](https://mlsanity.com/paper/2310.16360/) (2023)
- [IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks](https://mlsanity.com/paper/2410.20953/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning](https://mlsanity.com/paper/2606.17020/) (2026)
- [GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception](https://mlsanity.com/paper/2608.27971/) (2026)
- [ISTD-YOLO: A Multi-Scale Lightweight High-Performance Infrared Small Target Detection Algorithm](https://mlsanity.com/paper/2504.14289/) (2025)
- [SATGround: A Spatially-Aware Approach for Visual Grounding in Remote Sensing](https://mlsanity.com/paper/2512.08881/) (2025)
- [FlyPose: Towards Robust Human Pose Estimation From Aerial Views](https://mlsanity.com/paper/2601.05747/) (2026)
- [Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioning](https://mlsanity.com/paper/2603.12832/) (2026)
- [Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery](https://mlsanity.com/paper/2604.19233/) (2026)
- [Edge-Aware Thermal Infrared UAV Swarm Tracking](https://mlsanity.com/paper/2607.12544/) (2026)

## Abstract (arXiv)

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset.