Identification of autonomous landing sign for unmanned aerial vehicle based on faster regions with convolutional neural network

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 paper, we design the landing sign of the combination of concentric circles and pentagon, 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. Then, the estimation algorithm of position and direction based on vision is proposed. Position and direction for the UAV landing can be obtained based on least squares ellipse fitting and Shi-Tomasi corner detection method after the landing sign is effectively identified by Faster R-CNN. 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. The position and direction estimation error of the vision algorithm is within the allowable range, and it can meet the UAV real-time landing requirements.