Deep learning-based visual navigation control method for autonomous trajectory of UAVs

In this paper, a UAV intelligent visual navigation system is designed based on deep learning. To convert the pixel gray values, a Gaussian smoothing function is employed, which ensures that the main features of the visual image are preserved. A convolutional neural network is employed to mark the target with a frame using image pixels and obtain the coordinate position of the center point. Finally, the initial particles generated near the beacon are analyzed by particle filtering with color histograms, which are used to predict the position of the UAV at each autonomous trajectory point location. The control method proposed in this paper can keep the UAV attitude angle control error within 15%, and the minimum velocity error is 0.07%, as shown in the results. A deep learning-based visual navigation control system can guarantee that the UAV can accurately recognize the target in every autonomous trajectory.