# Vision Meets Drones: A Challenge

arXiv: https://arxiv.org/abs/1804.07437 · HTML: https://mlsanity.com/paper/1804.07437/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 30 of 81)

- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water](https://mlsanity.com/paper/2105.01922/) (2021)
- [A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization](https://mlsanity.com/paper/2201.09206/) (2022)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021](https://mlsanity.com/paper/2202.11983/) (2022)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images](https://mlsanity.com/paper/2303.14488/) (2023)
- [UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery](https://mlsanity.com/paper/2112.10415/) (2021)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.12252/) (2021)
- [Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning](https://mlsanity.com/paper/2311.11646/) (2023)
- [Focus-and-Detect: A Small Object Detection Framework for Aerial Images](https://mlsanity.com/paper/2203.12976/) (2022)
- [Dogfight: Detecting Drones from Drones Videos](https://mlsanity.com/paper/2103.17242/) (2021)
- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2410.11125/) (2024)
- [Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications](https://mlsanity.com/paper/2503.20516/) (2025)
- [ESOD: Efficient Small Object Detection on High-Resolution Images](https://mlsanity.com/paper/2407.16424/) (2024)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection](https://mlsanity.com/paper/2504.20670/) (2025)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [A DeNoising FPN With Transformer R-CNN for Tiny Object Detection](https://mlsanity.com/paper/2406.05755/) (2024)
- [AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes](https://mlsanity.com/paper/2106.10409/) (2021)
- [MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?](https://mlsanity.com/paper/2512.23219/) (2025)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing](https://mlsanity.com/paper/2101.12694/) (2021)
- [Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion](https://mlsanity.com/paper/2312.11008/) (2023)

## Abstract (arXiv)

In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform. The images and video sequences in the benchmark were captured over various urban/suburban areas of 14 different cities across China from north to south. Specifically, VisDrone2018 consists of 263 video clips and 10,209 images (no overlap with video clips) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. With intensive amount of effort, our benchmark has more than 2.5 million annotated instances in 179,264 images/video frames. Being the largest such dataset ever published, the benchmark enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. In particular, we design four popular tasks with the benchmark, including object detection in images, object detection in videos, single object tracking, and multi-object tracking. All these tasks are extremely challenging in the proposed dataset due to factors such as occlusion, large scale and pose variation, and fast motion. We hope the benchmark largely boost the research and development in visual analysis on drone platforms.