## 1 Introduction
[27] proposes a functional division of intelligent inland vessels (e.g., intelligent navigation, hull, power plant, and energy management), indicating the development of intelligent technologies in the 2030, 2035, and 2050 perspectives. In parallel, researchers are also working on systems such as real-time traffic object detection for autonomous vehicles [28]. In general, processing imaging data with vision systems has various applications, such as inspections, analyses related to object detection and classification, environmental health monitoring, anomaly detection, and many more. Vision systems also have many advantages, such as accuracy and speed of detection, adaptation to changing conditions, or the elimination of human errors. This allows them to operate in real-time, detecting signs with high precision, even in harsh environmental conditions. By using the learning capacity of the neural networks used in them, they can be trained on different data sets. This translates into their effectiveness in various environmental conditions. An undoubted advantage may also be the ability to support the ship's crew in the process of identifying shipping marks, which automatically translates into a reduction in the risk of human error. The above features of the systems can bring many benefits, including increasing the safety of navigation, automating navigation processes, or preparing this technology for tasks related to autonomous inland waterway transport. The schematic role of vision systems in inland shipping sign detection is illustrated in Figure 1.  
Deep learning is the foundation of modern vision systems, enabling automatic image analysis, including the detection and classification of various objects. Models based on neural networks, such as convolutional neural networks, are often used to perform these tasks [29]. In order to create an effective model, the network must be properly trained, which is often associated with the use of large data sets in the training process. Given this aspect, you can quickly conclude that training deep learning models is a computationally intensive process. In addition to the factor related to the size of the data (often tens of thousands of data or even millions), the computational complexity is influenced by the need to optimize a large number of parameters during the training process and the multiple processing of the same data (number of epochs). Selected problems related to the deep learning are presented in [30; 31; 32]. Graphics cards (GPUs) play a key role in accelerating deep learning calculations [33; 34] thanks to the ability to perform many parallel mathematical operations. GPUs are optimized for matrix computing, which is the foundation of deep learning algorithms. Compared to traditional processors (CPUs), GPUs offer significantly higher performance for data-intensive applications. Many machine learning libraries, such as TensorFlow [35; 36] and PyTorch [37], are designed to take advantage of GPU computing power. Choosing the right graphics card, therefore, has a direct impact on the efficiency and duration of the neural model training process. In addition, if we take into account the tasks related to the detection and classification of objects in real-time [38], vision systems require rapid data processing. It is clear that computer components, such as powerful GPUs, not only speed up training but also enable faster inference, which is crucial for navigation systems operating in dynamic environments. It is also safe to assume that as deep learning models become more complex and datasets become larger, hardware requirements will continue to increase. High performance of hardware, especially graphics cards, is therefore a key element enabling the practical application of deep learning in vision systems. Choosing the right hardware configuration is important not only for the effectiveness of training the models but also for their real-time applications, such as navigation systems for inland navigation.