# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 2 Literature Review  
Fishing vessel security is a key concern in diverse places, which is influenced by operator error, weather circumstances, technological variables, and environmental concerns, among others. According to [PERSON] et al. [12], weather-related factors were responsible for the majority of fishing vessel accidents, including tilting and sinking episodes, which accounted for almost 60% of all incidents. Collisions and bumps, which were a representation of environmental conditions, made up about 8.9% of all events. Therefore, to protect the sustainability of fisheries resources and guarantee the safety of fishing vessels and crew members, proactive monitoring of the operational state of fishing vessels is essential. Various studies have been conducted to enhance warning systems to reduce fishing vessel accidents, thus improving the safety of fishing vessels, which can be grouped into several categories. Various studies have been conducted to enhance the safety of fishing vessels by monitoring vessel attitude and environmental conditions. [PERSON] et al. [13] designed a ship-borne off-axis laser warning sensor that can provide 360\({}^{\circ}\) protection for the entire ship. In addition, a field test was conducted to confirm the idea under operating circumstances. [PERSON] and [PERSON] [14] employed a laser distance sensor and pan-tilt-zoom camera to monitor the distance of the target vessel and surrounding obstacles in real time. [PERSON] et al. [15] used laser range finding to display real-time information, enhancing the system's response speed and accuracy. However, this approach is linked to expensive equipment cost, making it less suitable for widespread implementation in smaller vessels. Another category of research focuses on utilizing vision sensors for guaranteeing vessel safety. [PERSON] et al. [16] proposed a monocular vision system capable of estimating target distance through horizon detection. If the detected distance surpasses a predefined threshold, the system promptly alerts the crew to execute a course change. [PERSON] et al. [17] designed a camera-based structure that allows for visual sensors to be used alone or in combination with data from vessel traffic services to create a system for monitoring vessel traffic. [PERSON] et al. [18] accomplished the ship tracking problem by extracting robust and highly correlated ship descriptors from several ship feature sets using a multi-view learning technique. In order to ensure the safety of ships, [PERSON] et al. [19] created a wide-field optical imaging system according to a vision-based ship speed measuring model. However, this approach is vulnerable to constraints in the field of view and target occlusion due to its high algorithm complexity, lighting and ambient requirements, and image processing and computation needs. Another category is using telecommunication systems like the Automatic Identification System (AIS) or the Global Navigation Satellite System (GNSS). [PERSON] et al. [20] developed a new structure for real-time AIS data collection, processing, storing, and analysis as well as a set of algorithms for scalable and effective data collection. [PERSON] and [PERSON] [21] proposed a solution for monitoring the vessel status in real time using an AIS temporal database. [PERSON] et al. [22] used a mobile device to create a localized AIS monitoring application that accurately tracks sea vessels' locations and movements, aiding vessels safety early warning. In addition, the mobile application provides a variety of information to ensure vessel's safety, including viewing AIS anywhere, accessing the functionalities of the AIS, and providing information on the other ships in the area of the target ship. [PERSON] and [PERSON] [23] presented a data-fusion-based tracking method that uses AIS to interpret radar data, so as to enhance vessels safety early warning. [PERSON] et al. [24] detected vessel avoided collision behavior using an enhanced sliding window method based on AIS trajectory data.