2024
DOI: 10.3390/rs16050756
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Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

Ying-Chih Lai,
Tzu-Yun Lin

Abstract: With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of t… Show more

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Cited by 2 publications
(2 citation statements)
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“…In article [28], a joint motion mechanism based on a three-degree-of-freedom (DOF) framework was designed for drones in complex motion patterns to achieve real-time active tracking of targets. Lai et al [29] proposed a background subtraction method to detect moving targets and used the Mask R-CNN model to identify target types. Article [30] provides an overview of the 2022 L4S competition aimed at overcoming the challenges of detecting landslides in remote sensing images.…”
Section: Rs Object Detectionmentioning
confidence: 99%
“…In article [28], a joint motion mechanism based on a three-degree-of-freedom (DOF) framework was designed for drones in complex motion patterns to achieve real-time active tracking of targets. Lai et al [29] proposed a background subtraction method to detect moving targets and used the Mask R-CNN model to identify target types. Article [30] provides an overview of the 2022 L4S competition aimed at overcoming the challenges of detecting landslides in remote sensing images.…”
Section: Rs Object Detectionmentioning
confidence: 99%
“…The utilization of traditional computer vision techniques presents a significant advantage over deep neural networks due to the latter's reliance on extensive training datasets. Such datasets, particularly those concerning mid-air collisions, are exceptionally challenging to acquire, rendering traditional methods more feasible and effective in this context [12].…”
Section: Introductionmentioning
confidence: 99%