## 1 Introduction  
Around ports and secure areas at sea, vessels must comply with certain rules to avoid congestion, accidents, and theft. Therefore, detecting vessels accurately is essential for sea operations in general and, in particular, for ports and secure areas at sea. Research on the detection methods of port vessels is of great significance for realizing the real-time detection of vessels and improving the efficiency of port vessel monitoring systems, especially for ports that use surveillance camera systems. With the development and application of modern sea monitoring systems at various ports, most of the world's major ports are building extensive CCTV surveillance systems at sea. These ports are committed to building real-time, intuitive, and whole-process CCTV monitoring systems, as shown in Figure 1[1]. Many ports have vessel traffic service (VTS) systems in place to monitor vessels and activities around them. These VTS systems integrate radar information, DGPS information, and AIS data to accurately monitor vessels at long distances. However, other ports use surveillance camera systems. According to the geographical characteristics of the monitoring system's control area and the sources of on-site image interference, the characteristics of port surveillance videos are analyzed and summarized in different ways, as follows:  
1. The target vessel has low contrast: In most cases, the surveillance camera is far away from the target vessel, owing to the relatively vast sea surface. The target vessel only occupies a few pixels in the video image, and its color is relatively close to that of the sea surface background. When the visibility of the sea surface is poor, it is difficult to spot the target in the image [2]. 2. The noise interference is high, and the sea environment is complex: Regional changes caused by waves on the sea surface are similar to the shape and size of the target vessel, which easily leads to the false detection of vessels. Large areas of sea surface ripples are difficult to remove using common filtering methods. Changes in lighting and the movement of clouds cause background changes over large areas in port video images [3]. 3. The vessel moves slowly: Under long-distance observation, the position of the target vessel in an image changes slowly and the difference between two images is only a few pixels. This easily leads to the void phenomenon in the central area of the target vessel when using detection methods for moving targets [4]. 4. The real-time processing of video: Vessel detection methods based on surveillance videos not only ensure the accuracy of system detection but also require real-time video processing. In order to facilitate real-time observation of the test results by maritime regulators, the algorithm needs to be robust [5]. Recently, much research has been carried out in this field. [PERSON] et al. [6] proposed the use of range-Doppler processing to improve the signal-to-noise ratio (SNR) before computing the moving target's trajectory based on a continuously bistatic range. This technology uses Global Navigation Satellite Systems, which are reliable. However, the limited power budget offered by navigation satellites is a primary concern. A hierarchical method was proposed to detect vessels in inland rivers. Multi-spectral information is used to extract the regions of interest, and then the panchromatic band is used to extract vessel candidates. Then, a mixture of multi-scale deformable part models and a histogram of the oriented gradient are used to detect the connected components and the clustered vessels. Finally, a back-propagation neural network is applied to classify the vessel candidates [7]. The advantage of this method is that it can accurately detect vessels in inland rivers as well as in port areas. It can effectively eliminate background noise and congestion interference at sea. However, it considers several mixed algorithms, which makes using this method very complex and difficult. [PERSON] et al.