Abstract
In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure. Particularly, we investigate different versions of the YOLO objection detection method and compare their performances for the specific application of detecting a safe landing location for a UAV that has suffered an in-flight failure. We compare the performance of YOLOv3, YOLOv4, and YOLOv5l while training them by a large aerial image dataset called DOTA in a Personal Computer (PC) and also a Companion Computer (CC). We plan to use the chosen algorithm on a CC that can be attached to a UAV, and the PC is used to verify the trends that we see between the algorithms on the CC. We confirm the feasibility of utilizing these algorithms for effective emergency landing spot detection and report their accuracy and speed for that specific application. Our investigation also shows that the YOLOv5l algorithm outperforms YOLOv4 and YOLOv3 in terms of accuracy of detection while maintaining a slightly slower inference speed.
Highlights
Introduction and Related Worksunmanned aerial vehicles (UAVs) are extensively being used in many fields, such as traffic monitoring, surveillance, inspection, surveys, etc
The safety measure that we propose in this paper can help both autonomous UAVs and human-operated UAVs
Our goal is to find an algorithm that can be used on a Companion Computer (CC) for real-time applications, for emergency landing spot detection of UAVs that have suffered a system failure
Summary
UAVs are extensively being used in many fields, such as traffic monitoring, surveillance, inspection, surveys, etc. They have replaced choppers in recent years due to their higher mobility and flexibility [1]. The advancement of real-time deep learning algorithms with improved speed and accuracy is changing how UAVs are being utilized in modern society. UAVs have come to dominate aerial sensing research with the use of deep neural networks in the urban, environmental, and agricultural contexts [2]. We address a safety concern related to using UAVs in urban environments. We choose urban environments since the risk of catastrophic accidents because of faulty
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