关键词: 深度残差网络, 深度学习, 无人机（UAV）, 路径感知

Abstract: Autonomous navigation for unmanned aerial vehicles (UAVs) has achieved promising performances in a known or structured environment. However, the autonomous flight technology is still a challenge in the unstructured environment such as forests. Detecting a forest trail and following it would be the most efficient and safest way for UAV in a forested environment. Considering the difficulty of trail recognition in forests, a model of two-column deep neural networks (2CDNN) is proposed. The proposed method addresses this challenge in a different way by getting feature maps using histogram equalization and edge detection. Then the feature maps and RGB images are fed to a two-parallel deep residual network to extract texture and color features from forest images. Finally, UAV controlling command is generated according to classified results. The proposed method has been evaluated on IDSIA dataset and achieved accuracy of 91.31%, which delivers a 4.41% improvement than previous methods. The experiment results show that the proposed method can improve trail perception performance of UAVs in forests effectively and has some generalization and practical significance in the field of autonomous navigation.

Key words: deep residual network, deep learning, unmanned aerial vehicle (UAV), trail perception