# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology
### Integrated Perception  
With the continuous development and advancement of technology, integrated perception technology plays an increasingly important role in the field of visual perception. It realizes comprehensive understanding and accurate mapping of complex environments by integrating data from different sensors and utilizing image processing and machine learning algorithms. The core advantage of integrated perception is its ability to overcome the limitations of a single perception method and significantly improve the accuracy and reliability of target identification through the synergistic effect of multi-source data. For the fusion of AIS and vision technologies, AIS data can provide the vessel of interest with a unique Maritime Mobile Service Identity (MMSI), position coordinates, ground heading and ground speed, while cameras can directly display the visual appearance of the vessel. [PERSON] et al. (2023) fused AIS and vision data to improve maritime traffic surveillance and were able to obtain more accurate ship tracking results and motion characteristics, taking full advantage of multiple sources of data [106]. To address the lack of detailed information important for real-world applications in deep learning object detection methods, [PERSON] et al. (2024) developed a technique for fusing automatic identification system (AIS) data with ships detected in images to create a dataset enriched with ship images [107]. [PERSON] et al. (2024) also combined AIS data with visual data and showed that this technique outperformed the pure AIS data technique during the daytime and outperformed the pure visual data technique at night, providing more reliable data for collision avoidance [108]. For the fusion of visual data and radar, both electronic charts and radar are essential equipment in a ship's navigation system, and the fusion of the two ensures that the ship is able to clearly display the dangers on the sea surface. [PERSON] et al. (2020) proposed a deep learning-based data fusion algorithm which extracts robust features from radar images and merges marine radar and electronic chart data to provide more comprehensive information [109]. [PERSON] et al. (2021) proposed a radar image denoising algorithm based on the concept of the Generative Adversarial Network (GAN) with Wasserstein distance to reduce the noise interference brought by external factors and hardware and utilize the sparse theory to parallelize the high-frequency and low-frequency sub-band coefficients of detected images obtained by Fast Fourier Transformation to realize the fusion of images [110]. For the combination of visual and infrared data, [PERSON] et al. (2022) used refined feature fusion and Sobel loss for the fusion of marine infrared and visible images [111], while [PERSON] et al. (2023) introduced an integrated approach to visual and infrared detection and ranging, combining deep learning-based computer vision techniques with real-time physics-based EO/IR data processing algorithms [112], all of which enhance the ability of ships to navigate autonomously in a variety of marine environments. For the fusion of radar and AIS systems, in the maritime environment, radar signals are often blocked by islands and large ships, resulting in degradation of radar target detection and performance. [PERSON] et al. (2023) utilized a priori environmental knowledge from electronic nautical charts (ENCs) and information from automatic identification systems (AISs) to propose an AIS-Assisted Integrated Probabilistic Data Association (A-IPDA) algorithm, which is well able to counteract long-time target occlusion in complex environments at sea and maintain tracking continuity and tracking accuracy [113]. To address the fact that false echoes in radar images and packet loss in AIS data transmission can lead to uncertainty in ship speed and heading estimation, [PERSON] et al. (2023) proposeda ship speed and heading estimation method utilizing the fusion of radar sequential images and AIS data to quantitatively characterize the state of ship navigation [114].