Deep convolutional object detection and search area prediction for UAV tracking

Nicolas Boirel; Moulay A. Akhloufi

doi:10.1117/12.2586002

12 April 2021 Deep convolutional object detection and search area prediction for UAV tracking

Nicolas Boirel, Moulay A. Akhloufi

Proceedings Volume 11758, Unmanned Systems Technology XXIII; 117580E (2021) https://doi.org/10.1117/12.2586002
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Over the past few years, Unmanned Aerial Vehicles (UAVs) have known important progress in their technology, spreading their adoption and their use in various types of applications. More recently, researchers have become more interested in the use of multiple UAVs and UAV swarms. In this work, we are interested in the use of vision-based deep learning algorithms for UAVs tracking and pursuit. The goal here is to use recent deep learning object detection, coupled with a ‘Search Area’ prediction approach, to detect and track a target UAV from images captured by another UAV. The detected position outputs the necessary controls for real-time maneuvering and tracking. The proposed architecture was tested on different simulated conditions. The approach was able to process videos at high frame rates and get a mean average precision above 90%. The obtained results show the possibility of using vision-based deep learning for detecting and tracking UAVs.

Conference Presentation

Citation Download Citation

Nicolas Boirel and Moulay A. Akhloufi "Deep convolutional object detection and search area prediction for UAV tracking", Proc. SPIE 11758, Unmanned Systems Technology XXIII, 117580E (12 April 2021); https://doi.org/10.1117/12.2586002

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