## 2 Related Work  
Automatic detection, localization, and estimation of the 6D pose of the target are virtual steps in UAV intelligent logistics. Therefore, it is important to design an accurate and efficient intelligent-vision system. Previous related research [6; 7] mainly focused on target detection, obstacle avoidance, visual navigation, and environmental awareness for UAVs; however, few focused on intelligent logistics tasks. To fill this gap, our research mainly aims to customize a vision-guided system that involves object detection, target tracking, semantic segmentation, object classification, and 6D object pose estimation for UAV intelligent logistics. We review the related research on these vision tasks. **Object detection:** Object detection is one of the fundamental tasks in computer vision. Traditional methods usually extract features [8; 9] and use AdaBoost [10] to classify and detect targets. With the sharp development of deep learning, object detection methods based on neural networks have rapidly emerged, and the frameworks of these methods can mainly be categorized into two types: two-stage region-based methods, including [11; 12; 13], and one-stage region-free methods, including [14; 15; 16; 17]. Two-stage region-based methods are characterized by the extraction of anchor boxes through the Region Proposal. A large number of extracted anchor boxes will cause an imbalance between positive and negative samples and affect the training and inference speed of the model. One-stage region-free methods complete category prediction and location regression simultaneously to improve the speed and reduce memory consumption. Considering the real-time advantage, we adopted the SSD algorithm in our system, which is highly efficient in detecting objects of different sizes and shapes and is robust because of the multi-scale feature maps used for prediction. **Object tracking:** Object tracking is a valuable task that is widely used in sports broadcasting, security monitoring, unmanned vehicles, robots, and other fields. Traditional object tracking methods include discriminative correlation filters (DCF) [18], Kernel tracking [19], silhouette tracking [20], and point tracking [21]. Object tracking methods based on deep learning have progressively replaced traditional methods and have become mainstream in recent years. SiamFC [22] and SANet [23] are both successful representatives of deep-learning-based methods. With the proposition of attention methods, the transformer-based model shows excellent capabilities in various fields, such as natural language processing (NLP) and computer vision. TransTrack [24] and TrackFormer [25] have achieved strong performance in object tracking as a representation of the transformer-based model. **Semantic segmentation:** Semantic segmentation is a pixel-level classification task that aims to segment an image into different parts. Traditional semantic segmentation methods are based on clustering approaches with additional information from the edges and contours [26]. After the full convolution neural network (FCN) [27] was proposed, methods based on deep learning have gradually dominated the semantic segmentation field. U-Net [28] defined an encoder network to extract semantic information from images and a decoder network to output pixel-level classification results. Similarly, U-Net adds skip connections to fuse the complementary information of different layers. The pyramid pooling module (PPM) is recommended in PSPNet [29] to aggregate context information from different scales to upgrade the network's ability to acquire global information. To segment objects at multiple scales robustly, various spatial pyramid pooling (ASPP) methods have been proposed by DeepLab [30]. Transformer-based research [31] on semantic segmentation is also in the limelight. **Object classification:** Classification is a classic problem in the field of computer vision. Since Alexnet [32] was proposed, an era of deep learning has been proposed.