## 1 Introduction  
The rapid development of machine learning technology is opening up new fields of application in many fields, including water transport. Currently, artificial intelligence methods are very often used to support navigation processes. This is mainly related to the state of movement of the vehicle and, in many cases, to the prediction of its movement, detection of the vehicle, or its tracking [1, 2, 3, 4, 5, 6]. Real-time processing of image data, thanks to deep learning and advanced algorithms, allows for the automation of key processes such as object detection and classification. Undoubtedly, an important role in the application of machine learning is played by autonomous units [7, 8], which should \"think\" like humans. These units set a fairly significant trend related to the use of machine learning methods, which, in the future, could be aimed at the intelligent automation of processes in the field of water transport. In the context of inland navigation, this paper focuses on navigational signs [9] in the machine learning process, which play a key role in ensuring safety and enabling the efficient movement of vessels on waterways. Navigation marks are a basic element of the navigational infrastructure. They provide information on safe routes, warn of potential hazards such as shoals or obstacles, and regulate water traffic. Unlike shipping, inland navigation often takes place in restricted waters such as canals, rivers, and lakes, where precise navigation is particularly important. Reading or overlooking navigational signs can lead to collisions between ships or serious damage to infrastructure. Critical infrastructure includes bridges, which are often the main restriction of navigation. A collision with a bridge can have very serious consequences caused by the interruption of land transport and communication ways or the exclusion of the shipping route from operation for a longer period. The current lists of accidents on waterways [10; 11] indicate a continuous need to improve systems that will reduce them. The responsibility for reading and interpreting the signs lies with the ship's crew. However, in the case of making mistakes, the human factor is a common one, often associated with fatigue, distraction, lack of proper navigational knowledge, or simply neglect of professional duties. Difficulties in interpreting navigation signs may also result from changing weather conditions, such as fog, rain, or generally limited visibility. The solution to the above problems may be the use of vision systems for ship detection, recognition, identification, and tracking [12; 13; 14; 15; 16; 17; 18], supported often by machine learning algorithms. The increasing role of vision sensors in autonomous vehicles can now also be noted. Taking into account the increased efficiency in information acquisition and its automated processing, data fusion, including cameras, is often used. Examples of such fusions are the combination of radar [19] and lidar [20]. Nowadays, various navigation processes are often supported by artificial intelligence methods, e.g., those related to the detection and tracking of objects in the marine environment. The spectrum of the use of cameras as part of a vision system is quite broad. This includes the detection and tracking of people, including the analysis of camera and thermal imaging data [21], analysis of algorithms for detecting vessels [22], deep neural network optimization methods for detecting marine objects on low-energy devices (edge devices) [23], the use of YOLO (v5 and v8) for real-time vessel detection and classification [24], the integration of stereovision imagery with MOOS-IvP for improved obstacle detection, and real-time USV navigation [25]. In the development aspect, the role of intelligent systems should be emphasized, including for inland vessels [26]. Ref.