Edge-accelerated UAV operations:a case study of open source solutions

This study explores the execution of AI algorithms on open Unmanned Aerial Vehicles (UAVs) equipped with Beagle-Bone AI-64 (BBAI-64) boards, comparing their performance to high-performance computers equipped with GPUs. Key factors are evaluated, such as inference time, end-to-end processing time, CPU usage, or temperature on the board. Furthermore, this study presents the development of an open UAV platform based on an open-source flight controller (Durandal) executing an open-source autopilot (ArduPilot). This platform facilitates the integration of various sensors or cameras, regardless of brand or communication protocol. The study’s key findings show that the BBAI-64 offers advantages for smaller Artificial Intelligence (AI) models, and achieving comparable performance for larger models with high-performance computers. This work contributes to optimising AI execution on UAVs and supporting the development of versatile, sensor-agnostic open-source UAVs

Face verification algorithms for UAV applications:an empirical comparative analysis

Unmanned Aerial Vehicles (UAVs) are revolutionising diverse computer vision use case domains, from public safety surveillance to Search and Rescue (SAR), and other emergency management and disaster relief operations. The growing need for accurate face verification algorithms has prompted an exploration of synergies between UAVs and face verification. This promises cost-effective, wide-area, non-intrusive person verification. Real-world human-centric use cases such as a ”Drone Guard Angel” for vulnerable people can contribute to public safety management and offload significant police resources. These scenarios demand efficient face verification to distinguish correctly the end users for authentication, authorisation and customised services. This paper investigates the suitability of existing solutions, and analyses five state-of-the-art candidate face verification algorithms. Informed by the advantages and disadvantages of existing solutions, the paper proposes an extended dataset and a refined face verification pipeline. Subsequently, it conducts empirical evaluation of these algorithms using the proposed pipeline and dataset in terms of inference times and the distribution of the similarity indexes. Furthermore, this paper provides essential guidance for algorithm selection and deployment in UAV-based applications. Two candidate algorithms, ArcFace and FaceNet512, have emerged as the top performers. The choice between them will depend on the specific use case requirements

Next-gen Industry 4.0 with 5G:enabling secure and high-performance services for critical infrastructure

The advent of Industry 4.0 heralds a new era in manufacturing, driven by advancements in automation, IoT, and AI. Integral to this shift is the deployment of robust communication networks capable of real-time data exchange. Leveraging 5G technology, with its low latency and high bandwidth, is crucial in meeting these demands. However, integrating vertical services with 5G networks poses challenges. This paper, part of the 5 G-INDUCE project, focuses on deploying and validating corrosion inspection and intruder surveillance services for critical infrastructures. Trials conducted at the Greek Experimentation Facility showcased successful service deployment, configuration, and high-definition video streaming. Quantitative results exceeded expected Key Performance Indicators, demonstrating the platform’s efficacy in integrating advanced network applications. This work contributes to the evolution of Industry 4.0 by harnessing the transformative potential of 5 G technology

Empirical comparison of face verification algorithms from UAVs

Face verification use cases have recently gained momentum in the increasingly digitalised society, and thus the need arises significantly to integrate this technology in wireless/mobile networked systems such as 5G and applications such as Unmanned Aerial Vehicle (UAV) based public safety services. However, there is no benchmarking result for the evaluation of the various existing face verification algorithms for such UAV applications. This paper is concerned with such new use cases (e.g., the Drone Guard Angel in the EU H2020 project ARCADIAN-IoT and the surveillance network applications in the EU H2020 project 5G-INDUCE), and provides an empirical comparison among three popular state-of-the-art face verification algorithms for this use case. To this end, a face verification pipeline is presented. These algorithms are then compared in terms of their inference time, and the distribution of the similarity indexes for different distances in UAV-based use cases. Their strengths and weaknesses are analysed, leading to an insightful recommendation on their applicability scenarios for UAVs

Dynamic-distance-based thresholding for UAV-based face verification algorithms

Face verification, crucial for identity authentication and access control in our digital society, faces significant challenges when comparing images taken in diverse environments, which vary in terms of distance, angle, and lighting conditions. These disparities often lead to decreased accuracy due to significant resolution changes. This paper introduces an adaptive face verification solution tailored for diverse conditions, particularly focusing on Unmanned Aerial Vehicle (UAV)-based public safety applications. Our approach features an innovative adaptive verification threshold algorithm and an optimised operation pipeline, specifically designed to accommodate varying distances between the UAV and the human subject. The proposed solution is implemented based on a UAV platform and empirically compared with several state-of-the-art solutions. Empirical results have shown that an improvement of 15% in accuracy can be achieved.</p