# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.1 Perception Technology  
The sensing technology of ship navigation needs to use radar, LIDAR, ship automatic identification system (AIS), probe, camera and other sensors to collect environmental data when ships are sailing and build intelligent algorithms such as perception enhancement, data fusion, target classification, decision recommendation, etc., to process and analyze the sensed data automatically, distinguish potential dangers and abnormalities, and at the same time formulate contingency measures, such as adjusting the ship's sailing routes and speed, so as to improve the safety of navigation [16]. For smart ships, smart perception technology may have been developed to a more advanced stage. Ship identification, static obstacle perception, visibility impact, speed perception, distance perception, observation angle and cost are all issues that still need to be addressed [12]. Currently, radar is commonly used to detect targets, but the echo of radar cannot feedback the shape and appearance of the target, while the long distance and the motion of the ship itself will affect ship perception, so [PERSON] et al. (2019) proposed a novel ship recognition and tracking system based on a deep learning framework, using a deep residual network and cross-layer jump connection strategy to extract advanced ship features to improve the recognition classification accuracy [17]. [PERSON] et al. (2019) proposed a new method for ship detection based on single-channel synthetic aperture radar (SAR) image complex signal klick (CSK), which first detects the potential location of a ship based on the area proposal and then obtains the final ship target based on the target identification, which is free from the influence of false alarms caused by radio-frequency interferences (RFIs) and avoids the leakage of the detection situation under dense targets [18]. During navigation, the position of the ship to be sensed is often uncertain, and the problem of recognizing multiple types of ships is also faced, all of which undoubtedly add to the difficulty of perception. [PERSON] et al. (2022) proposed a real-time ship collision risk perception model based on two domain proximity parameters, DDV (Degree of Domain Violation) and TDV (Time to Domain Violation), which adopts the inverse computation method and incorporates the information of the home route in the parameter computation and effectively solves the problem caused by the uncertainty of ship position prediction [19]. In order to achieve the detection and recognition of multiple types of ships, [PERSON] et al. (2021) improved the YOLOv5s algorithm by optimizing the loss function, regrouping the K-mean of the target initial frame at the data input side and enlarging the sensory field of view at the output frame, which greatly improved the precision and accuracy of ship image detection [20]. The variability of the maritime environment poses many challenges for the development of perception techniques; tracking a moving vessel in the context of moving ocean dynamics is one of them, and researchers overcame the challenge of tracking and recognizing moving vessels at sea by using background subtraction with real-time approximation of curve evolution based on level sets to delineate the contours of a moving vessel in the ocean [21]. Figure 3: The relationships between navigation technologies. At present, the research on navigation systems is still inseparable from environment perception in rain, snow, haze, low-light and other bad weather conditions; it is difficult for navigation systems to realize fast and accurate environment perception and meet the navigation and safety requirements of ships [22]. To ensure that ship detection remains accurate in low visibility, [PERSON] et al. (2022) proposed a low-visibility enhancement network (called LVENet) based on the Retinex theory combined with depth-separable convolution and further proposed synthetic degraded image generation and hybrid loss functions to enhance visibility under low-light imaging conditions [23]. [PERSON] et al.