## 2 Literature Review
### Visual Object Detection and Tracking  
With the continuous advancement of computer vision technology, visual detection and tracking methods based on deep learning have been widely applied in the field of ship detection and tracking, owing to their outstanding performance. The use of neural networks as the basis of algorithms has become a mainstream trend for classification and detection tasks, and such algorithms are divided into two main routes. The first comprises two-stage algorithms based on region recommendation, including R-CNN [24], Fast R-CNN [25], and Faster R-CNN [26]. This route essentially inherits the idea of traditional detection algorithms, where regions that may contain targets are first screened, followed by feature extraction and classification. The second route comprises end-to-end one-stage algorithms, such as the YOLO series [27; 28; 29; 30] and SSD [31]. These algorithms directly transform the bounding box location problem into a regression problem, thus reducing computational complexity and enabling the network to detect targets with a lightweight architecture. Specifically, [PERSON] et al. used the Mask R-CNN algorithm and Faster R-CNN algorithm, respectively, to construct ship target feature extraction and a recognition model, and compared the accuracy and performance of these two algorithms in target detection and classification by classifying warships and civilian ships [32]. Aiming at the problem of traditional shipboard radar having difficulty performing the task of detecting near-small ships, [PERSON], [PERSON] et al. proposed a small-ship detection method based on an improved GAN and CNN, and the experimental results show that their method significantly improved the detection effect of small ships [33]. To address the challenges of missed detection and misidentification in ship target detection within SAR images under complex scenes, [PERSON], [PERSON] et al. proposed a CSD-YOLO model based on YOLOv7. This model was validated on the SSDD and HRSID datasets, demonstrating significant improvements over Faster R-CNN, FCOS, YOLOv3, YOLOv5s, and YOLOv7 algorithms, with marked enhancements in multi-scale ship target feature extraction [34]. To address the challenges of low-visibility weather scenarios, [PERSON] et al. developed an enhanced Convolutional Neural Network (CNN) to improve ship detection under different weather conditions, and experimental results  
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under different monitoring conditions showed that their method outperforms SSD, Faster R-CNN, YOLOv2, and YOLOv3 in terms of detection accuracy [35].  
In recent years, scholars have explored the application of visual detection and tracking technology for information mining in maritime videos. [PERSON] et al. applied binocular stereo vision technology to the recognition and estimation of the depth of inland waterway ships and used the MobileNetV1 network as the feature extraction module of the YOLOv4 model for ship detection, greatly reducing the amount of computation while ensuring accuracy. In addition, a sub-pixel-level feature point detection and matching algorithm was used for depth estimation, which enriched the ship's perception of the navigation environment [36]. [PERSON] et al. proposed a deep learning-based framework for extracting ship speeds in haze environments, used the YOLOv5 detector and Deep SORT tracker to detect and track the ship, and estimated the ship speed by calculating the mapping relationship between image space and real space, which provided important value for guaranteeing the safety of ship navigation [37].