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Article

Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation

Department of Aeronautics and Aeronautics, National Cheng Kung University, Tainan 701, Taiwan
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Remote Sens. 2020, 12(18), 3035; https://doi.org/10.3390/rs12183035
Received: 27 July 2020 / Revised: 12 September 2020 / Accepted: 14 September 2020 / Published: 17 September 2020
Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. A quadrotor is regarded as an owned UAV, and it is able to estimate the distance of an incoming fixed-wing intruder. The adopted object detection method is based on the you only look once (YOLO) object detector. Deep neural network (DNN) and convolutional neural network (CNN) methods are applied to exam their performance in the distance estimation of moving objects. The feature extraction of fixed-wing UAVs is based on the VGG-16 model, and then its result is applied to the distance network to estimate the object distance. The proposed model is trained by using synthetic images from animation software and validated by using both synthetic and real flight videos. The results show that the proposed active vision-based scheme is able to detect and track a moving UAV with high detection accuracy and low distance errors. View Full-Text
Keywords: unmanned aerial vehicle (UAV); you only look once (YOLO); deep neural network (DNN); convolutional neural network (CNN); object detection; sense and avoid (SAA); mid-air collision avoidance unmanned aerial vehicle (UAV); you only look once (YOLO); deep neural network (DNN); convolutional neural network (CNN); object detection; sense and avoid (SAA); mid-air collision avoidance
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MDPI and ACS Style

Lai, Y.-C.; Huang, Z.-Y. Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation. Remote Sens. 2020, 12, 3035. https://doi.org/10.3390/rs12183035

AMA Style

Lai Y-C, Huang Z-Y. Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation. Remote Sensing. 2020; 12(18):3035. https://doi.org/10.3390/rs12183035

Chicago/Turabian Style

Lai, Ying-Chih, and Zong-Ying Huang. 2020. "Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation" Remote Sensing 12, no. 18: 3035. https://doi.org/10.3390/rs12183035

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