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Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization

arXiv 2110.14491 ↗First posted 27 Oct 2021cs.CV cs.AI cs.ROcited by 0

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Abstract (arXiv)

We consider the task of visually estimating the pose of a human from images acquired by a nearby nano-drone; in this context, we propose a data augmentation approach based on synthetic background substitution to learn a lightweight CNN model from a small real-world training set. Experimental results on data from two different labs proves that the approach improves generalization to unseen environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 27 Oct 2021.

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16 of 22 references.

References · 16

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  3. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 294
  4. 2019Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Datacited by 246
  5. 2019Active Domain Randomizationcited by 170
  6. 2019Learning Data Augmentation Strategies for Object Detectioncited by 139
  7. 2018Data Augmentation using Random Image Cropping and Patching for Deep CNNscited by 88
  8. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  9. 2017Smart Augmentation - Learning an Optimal Data Augmentation Strategycited by 54
  10. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 44
  11. 2018Improved Mixed-Example Data Augmentationcited by 42
  12. 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 28
4 more
  1. 2021Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVscited by 24
  2. 2018A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Dronescited by 23
  3. 2018Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Dronescited by 15
  4. 2018Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approachescited by 1

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