mlsanityData to 14 Oct 2026

Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles

arXiv 2112.12252 ↗First posted 22 Dec 2021cs.CVcited by 22

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Abstract (arXiv)

Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detection from UAVs by analyzing multiple training strategies across several models. Furthermore, we analyze several different data generation and sampling parameters to provide actionable engineering advice for further scientific research. The DeepGTAV framework is available at https://git.io/Jyf5j.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 35 of its 71 references that are in the map. First posted on arXiv 22 Dec 2021.

Builds on

35 of 71 references.

References · 35

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2018YOLOv3: An Incremental Improvementcited by 2,659
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  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
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  10. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  11. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  12. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
23 more
  1. 2018Domain Adaptive Faster R-CNN for Object Detection in the Wildcited by 443
  2. 2017Learning from Synthetic Humanscited by 359
  3. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  4. 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 208
  5. 2016Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?cited by 194
  6. 2017Drone-based Object Counting by Spatially Regularized Regional Proposal Networkcited by 156
  7. 2019Meta-Sim: Learning to Generate Synthetic Datasetscited by 128
  8. 2019Synthetic Data for Deep Learningcited by 113
  9. 2018Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Datacited by 94
  10. 2016A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learningcited by 86
  11. 2018Vision Meets Drones: A Challengecited by 81
  12. 2021Enhancing Photorealism Enhancementcited by 74
  13. 2021SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Watercited by 61
  14. 2020AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillancecited by 56
  15. 2018A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Drivingcited by 54
  16. 2016UnrealCV: Connecting Computer Vision to Unreal Enginecited by 41
  17. 2019Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perceptioncited by 29
  18. 2018Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaptioncited by 27
  19. 2020The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentationcited by 19
  20. 2021Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizingcited by 8
  21. 2018Unlimited Road-scene Synthetic Annotation (URSA) Datasetcited by 3
  22. 2021Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVscited by 2
  23. 2019Multi Modal Semantic Segmentation using Synthetic Datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 3; 2025: 4; 2026: 3.

22 papers.

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  1. 2023Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performancescited by 16
  2. 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
  3. 2023High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimationcited by 10
  4. 2022Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadatacited by 9
  5. 2024Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodologycited by 6
  6. 2022SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Watercited by 5
  7. 2023Memory Maps for Video Object Detection and Tracking on UAVscited by 4
  8. 2023Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVscited by 4
  9. 2023Whale Detection Enhancement through Synthetic Satellite Imagescited by 4
  10. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  11. 20253rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Resultscited by 4
  12. 2023Assessing Domain Gap for Continual Domain Adaptation in Object Detectioncited by 3
10 more
  1. 2025REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Frameworkcited by 3
  2. 2023SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditionscited by 2
  3. 2025From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigationscited by 2
  4. 2025A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learningcited by 2
  5. 2023Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisonscited by 1
  6. 2024Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarioscited by 1
  7. 2024Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generationcited by 0
  8. 20264th Workshop on Maritime Computer Vision (MaCVi): Challenge Overviewcited by 0
  9. 2026A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasetscited by 0
  10. 2026PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysiscited by 0