UAVNet: An Efficient Obstacel Detection Model for UAV with Autonomous Flight

Abstract

Autonomous navigation for large Unmanned Aerial Vehicles(UAVs) is straight-forward to implement, just employ expensive and sophisticated sensors and monitoring devices. On the contrary, usual small quadrotor UAV still have the challenge on obstacle avoidance since this kind of UAV can only carry very light weight sensors such as cameras. Given the above reason, making autonomous navigation over obstacles on small UAV is much more challenging. In this paper, we focus on proposing a novel and memory efficient deep network architecture named UAVNet for small UAV to achieve obstacle detection in the urban environment. Compared with state-of-the-art DNN architecture, UAVNet has only 2.23M parameters(which is half compared with MobileNet) and 141 MFLOPs complexity. Though the parameters are fewer than usual, the accuracy is acceptable, about 80% validated on ImageNet-2102 dataset. To further justify the utility of UAVNet, we also implement the architecture on Nvidia TX2 in real environment using NCTU campus dataset. The experiment shows the proposed UAVNet can detect obstacles to 15 fps, which is a real-time application.