No abstract

UAV-based target positioning methods are in great demand in fields, such as national defense and urban management. In previous studies, the localization accuracy of UAVs in complex scenes was difficult to be guaranteed. Target positioning methods need to improve the accuracy with guaranteed computational speed. The purpose of this study is to improve the accuracy of target localization while using only UAV information. With the introduction of depth estimation methods that perform well, the localization errors caused by complex terrain can be effectively reduced. In this study, a new target position system is developed. The system has these features: real-time target detection and monocular depth estimation based on video streams. The performance of the system is tested through several target localization experiments in complex scenes, and the results proved that the system can accomplish the expected goals with guaranteed localization accuracy and computational speed.

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods.