## 3 Experimental Tests and Results Analysis  
To evaluate the algorithm, first of all, some images were chosen from the dataset already created combined with extra images captured using an ESP32-CAM with a OV2640 camera and a FOV 160\({}^{\circ}\) lent to obtain images in different conditions, such as harsh lighting, reflections, and angled shots. As a result, these images were grouped, making it possible to compare the results and analyze the influence of the different shooting environments. The first group was named the ideal conditions, which only has images with good lighting and front-facing gauges, making it easier to read the indicating value. The other two groups were composed of images with bad lighting conditions and different shooting angles. Since some of the gauges present scale takeup (the portion of the scale between the position where the pointer is stopped and its true zero pressure position), the definition of the angle for the first scale mark was set in the true zero pressure position to avoid reading errors. Furthermore, to make the tests consistent, only gauges with the same accuracy class were used, as these gauges followed the standard EN 837-1 [23]. Since most of the gauges in the images had an accuracy of 2.5%, these were the ones tested. For each gauge, the evaluation metric used was the relative error, \(E_{i}\), between the read indicated value and the value determined by the algorithm, and then the average relative error, \(\bar{E}\), was determined for all images and each group of shooting environment. These metrics are calculated using Equations (2) and (3). \[E_{i}=\frac{|r_{i}-R_{i}|}{S}\ \times\ 100\ \%\, \tag{2}\]  
\[\bar{E}=\frac{\sum_{i=0}^{n}E_{i}}{n}, \tag{3}\]  
where \(r_{i}\) is the reading value from the algorithm, \(R_{i}\) is the reading value determined visually, \(S\) is the range of the values, and \(n\) is the number of images. Out of the 20 images tested, only one was read incorrectly, which demonstrates the capability of the algorithm even in difficult conditions. However, for one image, the algorithm did not perform correctly because the Hough transform detected the dial circle as slightly deviated, which affected the polar transform and consequently the pixel projection; therefore, the maximum projection value was incorrect at the tale of the pointer. The accuracy was generally high, with an average relative error of 0.67% in ideal conditions. In challenging conditions, the algorithm almost doubles the error, which indicates that the bad lighting and angled gauges negatively affect the algorithm; nevertheless, the error calculated was still relatively low, with a value of 0.95%, as shown in Table 2. Therefore, the accuracy, especially in ideal conditions, is suitable for less critical commercial uses, which cover a wide range of current analog gauges. Furthermore, comparing the proposed solution with some state-of-the-art algorithms, the proposed algorithm performs relatively well, having a slightly better performance than most methods analyzed in Table 3. However, the [PERSON] et al. [18] solution performs the best out of all, which achieved high accuracy in difficult conditions of lighting and tilting gauges, but the tests were performed for only one gauge, which could result in low generalization and algorithm overfitting. This can explain the much better results in comparison with the other methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Shooting Environment** & **Average Relative Error (\%)** \\ \hline Ideal conditions & 0.67 \\ Bad lighting conditions & 1.28 \\ Different shooting angles & 1.23 \\ \hline All images & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average relative error for different shooting environments. In addition, an experiment that was only focused on the robustness was executed. It used the same 20 images from the previous experiment with some defined image adjustments to observe if the proposed solution was capable of reading gauges correctly.