2017-18

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Identification of Landing Sign of UAV Using Faster R-CNN Algorithm
(2018-09-01T10:36:04Z) Aman Kumar, Dubey; Anjali, Muralidharan; Dhanalakshmi, B
In order to realize autonomous landing of the unmanned aerial vehicle (UAV) in power patrolling, a visual method vision based on Faster Regions with Convolutional Neural Network (Faster R-CNN) for UAVs is studied. In this project, we design the landing sign of the Unmanned Aerial Vehicle(UAV’s) and propose the Faster R-CNN recognition algorithm which can be used to identify the target sign. Faster R-CNN successfully identifying the landing mark is the most important step for the UAV autonomous landing. The experimental results show that it can achieve recognition speed of nearly 81 millisecond each frame and 97.8% accuracy by using Faster R-CNN for detection and identification. The proposed method has better identification accuracy compared with three target identification methods, the Support Vector Machine (SVM) classification, the Back Propagation (BP) neural network and You Only Look Once (YOLO) based on deep learning.