Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

Drones play a crucial role in search-and-rescue missions by providing real-time information on areas of interest that are difficult to access or endangering to human rescuers. However, analyzing raw video feeds by human operators to detect objects of interest, such as vehicles or victims, becomes increasingly demanding as the mission duration increases. This underscores the need for embedded computer vision to reduce the operator’s cognitive load and enhance mission responsiveness. Towards this goal, we propose a low-latency object detection model based on YOLO, fitted to search-and-rescue missions, and able to process data coming from RGB and event cameras. We also propose a low-latency implementation on FPGA on board drones, achieving accurate detection in less than 20ms. Through a series of tests using a prototype drone, we highlight the features of the model and processing core that favor drone reactivity and operational autonomy.

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Publication Details

Published
2025-10-29
DOI
https://doi.org/10.1109/ssrr68451.2025.11391298
Primary Topic
UAV Applications and Optimization
Type
article
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article

Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

Hugo Le Blevec, Hajer Fradi, Matthieu Arzel, Mathieu Léonardon et al.
UAV Applications and Optimization
article

Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

Hugo Le Blevec, Hajer Fradi, Matthieu Arzel, Mathieu Léonardon, Isabelle Fantoni, Lucia Bergantin, Florian Pouthier, Panagiotis Papadakis, Jean-Philippe Diguet, Ismail Amessegher, Lionel Génevé, Kojo Nyamekye Anyinam-Boateng, Arthur Gaudard
article en

Abstract

Drones play a crucial role in search-and-rescue missions by providing real-time information on areas of interest that are difficult to access or endangering to human rescuers. However, analyzing raw video feeds by human operators to detect objects of interest, such as vehicles or victims, becomes increasingly demanding as the mission duration increases. This underscores the need for embedded computer vision to reduce the operator’s cognitive load and enhance mission responsiveness. Towards this goal, we propose a low-latency object detection model based on YOLO, fitted to search-and-rescue missions, and able to process data coming from RGB and event cameras. We also propose a low-latency implementation on FPGA on board drones, achieving accurate detection in less than 20ms. Through a series of tests using a prototype drone, we highlight the features of the model and processing core that favor drone reactivity and operational autonomy.

École Centrale de Nantes (FR), Centre National de la Recherche Scientifique (FR), Université de Bretagne Occidentale (FR), Laboratoire des Sciences du Numérique de Nantes (FR), Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance (FR), IMT Atlantique (FR), Canadian Nautical Research Society (CA), Nantes Université (FR)
Openalex Percentile: Top 55%
UAV Applications and Optimization
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