## 1 Introduction  
Recent advances in areas such as computer vision, automation, and artificial intelligence have created new opportunities in the aviation logistics industry. Future improvements require research on the automatic, unmanned, and informational directions that will power the efficiency and quality of delivery services. Therefore, the demand for unmanned aerial vehicle (UAV)-based automatic express delivery systems has increased rapidly over the past several decades. Currently, UAVs are widely used in the fields of surveillance, aero photography, military, and others [1, 2], while most research still focuses on control algorithms and path planning [3, 4]. Owing to the importance and widespread adoption of target localization and recognition in complex scenes, we proposed an efficient vision system, including object detection, tracking, segmentation, and 6D pose estimation, and deployed it on a UAV platform to demonstrate its potential in the applications of automatic express delivery. The proposed vision system can detect, locate, and track targets of interest without manual intervention by calculating precise position and pose parameters. In addition, we built a dataset to evaluate the performance of our algorithm, called the SIAT dataset, which contains 20 video sequences with a total of 13,161 frames. The ground truth of our SIAT dataset comprised segmentation masks and 6D pose parameters for all targets in each frame. As shown in Figure 1, the workflow for the entire system consists of the following stages:  
* **Object detection**: The RGB-D camera on the UAV can continuously capture images during flights and then transmit them to the server via the WIFI system. The target of interest can be detected and located by the single shot multibox detector (SSD) [5] algorithm. * **Object tracking**: Once the target is detected, the UAV will continue to track and approach it steadily. * **Semantic Segmentation**: The semantic segmentation network processes the image combined with the color and depth information and outputs an accurate segmentation mask. Furthermore, for objects with the same textures but different 3D geometries, as described in the subsection about object classification, we introduced a classification network to distinguish them. * **6D Pose Estimation**: The 6D pose estimation network calculates the pose parameters of the target in the segmented image and transmits them to the UAV. The details of the above are described in Section 3. The main contributions of this study are as follows. * We built a practical vision system and reliable visual assistance of express delivery that expands collaborative work between humans and UAVs by enabling accurate localization and 6D pose parameters of the targets. * We proposed a semantic segmentation network with a novel feature fusion structure, which provides more comprehensive semantic information for segmentation by connecting different features at different layers to fuse color and depth information. * We proposed an innovative 6D pose estimation network that uses the pyramid pooling module (PPM) and the refined residual block (RRB) module in the color feature extraction backbone to enhance features to accurately generate 6D pose parameters for the target. Figure 1: The hardware setup and workflow of the proposed vision system. (1) UAV: the UAV obtains pictures through the RGB-D camera, then transmits the data to the server via a WIFI system. (2) Server: the images are processed to detect, segment, classify the target of interest, and estimate its pose parameters. (3) Workflow: object detection: detect the target of interest in flight, the distance from the target is usually greater than 3 m; object tracking: approach and track the target, the distance to the target ranges from 1 to 3 m; semantic segmentation: segment the target pixels from the background and classify the target; 6D pose estimation: estimate the 6D pose parameters pixel-by-pixel in the target region.