## 1 Introduction
[8] proposed a Multi-Head Self-Attention-You Only  
Figure 1: Navigation system architecture. Look Once (MHSA-YOLO) method for ship object detection in inland rivers. To strengthen the feature information of the ship objects and reduce the interference of background information, the method incorporates MHSA into the feature extraction process. A more straightforward two-way feature pyramid is created throughout the feature fusion process to improve the fusing of feature information, resulting in an accuracy detection of 97.59%. However, its processing time is low (about 4.7 s to perform a correct detection). When it is getting dark, the detection accuracy is just 91.9%, meaning that this method does not perform well late in the evening. [PERSON] et al. [9] proposed the use of a block division technique, which splits images into tiny pixel blocks that correspond to small vessel or non-vessel regions. The blocks are classified using a support vector machine utilizing the texture and color information taken from the blocks. Finally, it makes use of a reconstruction algorithm, which extracts the identified vessels and fixes the majority of incorrectly categorized blocks. This method is more computationally efficient because it captures the characteristics of regions more accurately. This method has precision of around 98.14% for the final vessel detection and approximately 96.98% for the classification between vessel blocks and non-vessel blocks. However, the experiments were probably carried out using large vessels. This is because when this method was applied to small vessels, it was discovered that the detection accuracy was lower, with a precision of 93.3% and 90.8% for detection and classification, respectively. [PERSON] et al. [10] proposed a new inter-frame difference algorithm for moving target detection. Texture and color features are used in a two-frame-difference approach. Vessels can be effectively detected, with an accuracy of about 94.7% and a processing time of 2.01 s. However, the false alarm rate is higher for small vessels with low contrast. [PERSON] et al. [11] designed a vessel detection method with a monitoring system using an automatic identification system and a geographic information system. This vessel detection approach uses mixed Gaussian background modeling, but it results in a large void area in the center of the hull. The accuracy of the detection system is about 92%. However, the processing time is 3.76 s, which is considered poor in engineering applications. Additionally, this approach suffers from serious interference to background updates due to large variations in the sea surface illumination and rapid changes in the cloud background. A detection and classification method for ships based on optical remote sensing images was implemented successfully in the field of sea surface remote sensing [12]. This method has a very simple algorithm that is easy to understand. However, it suffers from a high false detection rate of 0.376. Another detection method using optical satellite imagery was proposed; it captures images in visible light [13]. However, the system relies on daylight and clear skies for efficient operation, making it susceptible to weather conditions and atmospheric interference. It cannot perform properly in poor lighting, which limits its capabilities. Additionally, there are many other methods that can be used for vessel detection, including the method of matching the coastline with the information stored in the Geographic Information System (GIS); the classification of regional homogeneity in an image; methods based on statistical models; methods based on deep learning models; methods based on the standardized difference water index; and more. All the methods mentioned above can effectively improve the SNR of an image, suppressing background interference. They can detect moving targets against a background with just a little noise. However, they detect small vessels without protecting the edge of the moving target signal from being blurred; this leads to congestion.