Robust landing pad detection plays a major role in Autonomous Unmanned Aerial Vehicles (UAVs). This problem can be approached using deep neural networks for vision-based inference. However, the full integration of deep learning algorithms into the small UAVs is still challenging for their limited resources. This paper presents a landing pad detection pipeline based on a revisited version MobileNetV3-Small. The proposed architecture inherits robustness from the general-purpose version but limits the computational cost significantly thanks to a set of design criteria aimed to limit hardware requirements. Experimental results confirm that the proposed network compares favorably with a lightweight general-purpose object detector in terms of accuracy/computational cost trade-off. The system is also deployed on a commercial general-purpose microcomputer confirming that satisfactory performance can be obtained on general-purpose embedded architectures.

Design and Deployment of an Efficient Landing Pad Detector / Albanese, Andrea; Taccioli, Tommaso; Apicella, Tommaso; Brunelli, Davide; Ragusa, Edoardo. - 546:(2022), pp. 137-147. ((Intervento presentato al convegno SYSINT 2022 tenutosi a Genova nel 7th–9th September 2022 [10.1007/978-3-031-16281-7_14].

Design and Deployment of an Efficient Landing Pad Detector

Albanese, Andrea
Co-primo
;
Brunelli, Davide
Penultimo
;
2022-01-01

Abstract

Robust landing pad detection plays a major role in Autonomous Unmanned Aerial Vehicles (UAVs). This problem can be approached using deep neural networks for vision-based inference. However, the full integration of deep learning algorithms into the small UAVs is still challenging for their limited resources. This paper presents a landing pad detection pipeline based on a revisited version MobileNetV3-Small. The proposed architecture inherits robustness from the general-purpose version but limits the computational cost significantly thanks to a set of design criteria aimed to limit hardware requirements. Experimental results confirm that the proposed network compares favorably with a lightweight general-purpose object detector in terms of accuracy/computational cost trade-off. The system is also deployed on a commercial general-purpose microcomputer confirming that satisfactory performance can be obtained on general-purpose embedded architectures.
Advances in System-Integrated Intelligence: Proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022)
Cham, CH
Springer Nature Switzerland AG
978-3-031-16280-0
978-3-031-16281-7
Albanese, Andrea; Taccioli, Tommaso; Apicella, Tommaso; Brunelli, Davide; Ragusa, Edoardo
Design and Deployment of an Efficient Landing Pad Detector / Albanese, Andrea; Taccioli, Tommaso; Apicella, Tommaso; Brunelli, Davide; Ragusa, Edoardo. - 546:(2022), pp. 137-147. ((Intervento presentato al convegno SYSINT 2022 tenutosi a Genova nel 7th–9th September 2022 [10.1007/978-3-031-16281-7_14].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/353241
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