# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 2 Related Work  
Inland waterways, due to their unique geometry and high traffic density, place significant pressure on waterway traffic situation awareness [9]. Traditional waterway information perception primarily relies on manual monitoring and the transmission of information via optical signals, which are not only inefficient but also susceptible to human error and environmental factors, making real-time, precise monitoring challenging. With the rapid advancement of deep learning technology, methods based on object detection, tracking, and multi-source data fusion have progressively been applied to waterway monitoring, substantially enhancing perception capabilities and management efficiency. In recent years, deep learning, leveraging the remarkable potential of convolutional neural networks (CNNs) in computer vision tasks, has been widely applied in image classification, image enhancement, and other image processing domains [10]. Numerous deep learning methods have been developed for object detection, including R-CNN [11], Fast-RCNN [12], Faster-RCNN [13], SSD [14], and the YOLO [15] series. Enhancements based on these object detection algorithms have found widespread application in the maritime domain. For instance, [PERSON] et al. incorporated the CA attention mechanism into YOLOv7-tiny, modified the SPP structure, and replaced the loss function with SloU, significantly improving the model's performance [16]. [PERSON] et al. introduced ConNextV2 and applied it to the YOLO network, effectively enhancing the model's performance across various recognition benchmarks [17]. [PERSON] et al. designed the channel attention pyramid structure of ECAP-YOLO, reducing information loss and optimizing detection accuracy [18]. [PERSON] proposed the FE-YOLO model, which combines feature fusion and enhancement techniques to notably improve marine target detection accuracy [19]. [PERSON] et al. introduced the Asymptotic Feature Pyramid Network (AFPN) and Large Selection Kernel Attention Mechanism (LSK) into YOLOv8, improving multi-scale feature fusion and suppressing background interference. This significantly enhanced the precision of multi-scale vessel target detection and the ability to recognize small targets, addressing issues related to small target miss detection and false alarms in complex environments [20]. [PERSON] proposed a network structure (CTF-Net) that integrates convolution and a transformer for ship detection, enhancing detection accuracy and robustness by balancing local feature extraction and global information modeling [21]. [PERSON] combined convolutional neural networks (CNNs) and the Swin transformer to achieve fine-grained vessel classification, improving recognition accuracy for various vessel types and optimizing the model's performance across diverse scenarios [22]. [PERSON] proposed a method for detecting dense inshore ships based on edge semantic decoupling and transformers, effectively improving detection accuracy and reducing false alarms and missed detections by incorporating ship edge semantic information and contextual information [23]. In the complex traffic environment of inland waterways, stable and accurate target-tracking technology is crucial for achieving fine-grained situation awareness. Computer vision-based target-tracking algorithms focus on specific targets, providing dynamic traffic situation information [24]. For instance, [PERSON] presents a vessel detection and tracking scheme for cage aquaculture visual surveillance, which effectively improves the accuracy and efficiency of vessel detection and tracking through robust foreground detection, background updating, fast four-connected component labeling, and wave ripple removal, thereby enhancing the security of cage aquaculture [25]. [PERSON] proposes four YOLO-based object recognition models and a new tracking algorithm to improve recognition accuracy and tracking stability in high-dynamic environments [26]. [PERSON] addresses the challenges in coastal ship tracking and proposes an enhanced SiamMask network.