Skip to main content

Simulating Aerial Event-Based Environment: Application to Car Detection

  • Conference paper
  • First Online:
European Robotics Forum 2024 (ERF 2024)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 32))

Included in the following conference series:

  • 64 Accesses

Abstract

With the primary goal of enhancing the efficiency of drones for research and rescue missions through the exploitation of neuromorphic sensors and event-based vision, our focus in this work lies in setting up a simulated environment that can be used for synthetic data generation. In particular, we employ Unreal Engine to generate scenes suitable for the case of vehicle perception, followed by a dynamic event-based simulation environment in interaction with the AirSim and v2e software tools. The synthetic event data acquired in this simulated environment is shown to provide a valuable resource for training Artificial Intelligence (AI) systems and more particularly for the task of car detection using YOLOv7.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.unrealengine.com/marketplace/en-US/product/modular-neighborhood-pac.

References

  1. Andrei, M., Panagiotis, P., Serge, G.: An open-source software framework for reinforcement learning-based control of tracked robots in simulated indoor environments. Adv. Robot. 36(11), 519–532 (2022)

    Article  MATH  Google Scholar 

  2. Cao, Y., et al.: VisDrone-DET2021: the vision meets drone object detection challenge results. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2847–2854 (2021)

    Google Scholar 

  3. Dimitrova, R.S., Gehrig, M., Brescianini, D., Scaramuzza, D.: Towards low-latency high-bandwidth control of quadrotors using event cameras. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4294–4300. IEEE (2020)

    Google Scholar 

  4. Fradi, H., Bracco, L., Canino, F., Dugelay, J.-L.: Autonomous person detection and tracking framework using unmanned aerial vehicles (UAVs). In: 2018 26th European Signal Processing Conference (EUSIPCO), pp. 1047–1051. IEEE (2018)

    Google Scholar 

  5. Gallego, G., et al.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 154–180 (2020)

    Article  MATH  Google Scholar 

  6. Hu, Y., Liu, S.-C., Delbruck, T.: v2e: from video frames to realistic DVS events. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1312–1321 (2021)

    Google Scholar 

  7. Kruijff, G.-J.M., et al.: Rescue robots at earthquake-hit Mirandola, Italy: a field report. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2012)

    Google Scholar 

  8. Ramachandran, A., Sangaiah, A.K.: A review on object detection in unmanned aerial vehicle surveillance. Int. J. Cogn. Comput. Eng. 2, 215–228 (2021)

    MATH  Google Scholar 

  9. Sanders, A.: An Introduction to Unreal Engine 4. CRC Press, Boca raton (2016)

    Book  MATH  Google Scholar 

  10. Shah, S., Dey, D., Lovett, C., Kapoor, A.: AirSim: high-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics: Results of the 11th International Conference, pp. 621–635. Springer (2018)

    Google Scholar 

  11. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

    Google Scholar 

  12. Zhao, H., Zhang, H., Zhao, Y.: YOLOv7-sea: object detection of maritime UAV images based on improved YOLOv7. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 233–238 (2023)

    Google Scholar 

  13. Zubić, N., Gehrig, D., Gehrig, M., Scaramuzza, D.: From chaos comes order: ordering event representations for object recognition and detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12846–12856 (2023)

    Google Scholar 

Download references

Acknowledgement

This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01 from September 2022 to December 2024. In this program, the project associated to this work is LEASARD (Low-Energy deep neural networks for Autonomous Search-And-Rescue Drones).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hajer Fradi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Amessegher, I., Fradi, H., Liard, C., Diguet, JP., Papadakis, P., Arzel, M. (2024). Simulating Aerial Event-Based Environment: Application to Car Detection. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_26

Download citation

Publish with us

Policies and ethics