## 4 Discussion  
The aim of the study was to conduct a comparative analysis of the usefulness of consumer graphics cards in the process of deep learning in the field of detection of navigational marks in inland navigation. Using WSM methods, a methodology for comparing cards was developed using various criteria and expert knowledge. The detection of navigational marks in inland navigation was only employed as a practical case study to demonstrate the methodology. Based on the results of the analysis, it can be concluded that the highest performance in terms of learning and inference was achieved by the Nvidia graphics card, which, at the same time, was characterized by the highest power demand. The results in Section 3.3 (Table 8) show that it often outperformed other designs in terms of speed, even many times over, while achieving the highest power efficiency of all the models tested. This combination of parameters makes Nvidia's solutions particularly beneficial for processing large data sets, such as video surveillance data for river navigational markings. For example, processing a 6 h video recorded at 25 frames per second (equivalent to 540,000 images) with YOLO 11 takes approximately 1 h and requires 422 kJ of electricity using the Nvidia RTX 4090. By comparison, the Intel ARC A750 performs this operation in approximately 3 h and consumes 1146 kJ of power, while the AMD RX 7900 XT needs approximately 1 h and 16 min and consumes 1107 kJ of electricity. Using two graphics cards in the case of AMD or Intel would reduce the processing time, but even with this solution, the energy efficiency would remain lower compared to a single Nvidia card, resulting in higher operating costs. Therefore, in the context of frequent processing of large data sets, the choice of a solution characterized by higher energy efficiency is crucial. Analysis of the performance results also indicates that individual graphics cards handle different frameworks differently. For example, the AMD RX 7900 XT shows significantly worse results in both speed and energy efficiency in the OpenCV framework (Section 3.4, Table 9) despite its formal compatibility with this software. This suggests that the choice of graphics card should depend not only on its overall performance but also on the specifics of the development environment used. When processing large image setswith OpenCV, the AMD card can generate excessive operating costs and extend the time of project implementation, which in extreme cases can influence their timely implementation. Our studies also enable more detailed analysis. Based on the results obtained from the performance measurements, it is possible to not only identify the overall best-performing graphics card for research related to vision systems in inland navigation according to the adopted criteria but also to pinpoint specific solutions to challenges associated with the future deployment of such systems. For instance, the data presented in Table 11 indicate that the Intel graphics card achieved the highest energy efficiency during inference using the YOLO v4 model within the OpenCV framework. Therefore, if the development of a vision system involves the use of this specific neural network and framework, the Intel graphics card appears to be the most advantageous in terms of energy consumption. Additionally, the inference time of 110 ms (Table 9) enables its use in real-time systems, such as sign detection at a frequency of nine frames per second. Consequently, the use of the Intel card may not only improve the system's energy efficiency but also reduce overall production costs due to the significantly lower price of the hardware. In turn, for the MobileNetV2 and ResNet50 models within the TensorFlow framework, the AMD graphics card demonstrates high inference energy efficiency (Table 11), which also supports its use in the design of cost-effective and energy-efficient systems.