## 5 Empirical Study
After further training, we test the model again over the similar images employed in the former case. These testing results are represented in the second column of Table 2. The model was able to achieve substantial performance improvement with the 83.8% for the mAP and 78.73% for the average IoU. To evaluate the performance of the produced model in various weather conditions, we formed four distinct testing data cohorts based on the distance to the artificial visual landmarks in the images. In our experiments, we employed images captured from the distance of 10, 20, 30, and 40 m. Figure 5 represents the performance demonstrated by the model on these data cohorts. For the sake of comparison, we also presented the results for the model trained on the data without affine transformations. From the results, one can see the employment of various augmentation techniques over the training data, wherein the quality was significantly deteriorated by extreme environmental conditions, which made it possible to substantially enhance the ML performance in the case of testing sample quality degradation. However, as the distance between the visual landmark and the camera increased, the tag recognition performance dropped. A distance of 30 m from the tag contributed to a 60% ML performance decline. Furthermore, when starting from a distance of 40 m away from the tag, the model completely stopped recognizing the tag. In addition to extreme weather conditions, we also considered changing illumination as another quality degradation factor. In the Arctic region, daylight is highly limited in most times of the year, which poses an additional challenge for AV computer-vision-based localization systems. To evaluate how our model was robust regarding the changes in illumination, we formed a number of testing data cohorts. They were generally divided into two categories: daylight, which incorporates images captured in the lightning conditions of 155 LUX, and nighttime, with images captured in the extremely limited lightning conditions of 5 LUX. Each of these categories included three sub-categories of images captured from the varied distances of 0.5, 2, and 5 m. Table 3 demonstrates the results of the ML model performance over the employed images cohorts. \begin{table}
\begin{tabular}{c c c} \hline \hline
**YOLOv4, \%** & **Haar Cascades, \%** & **HOG + SVM, \%** \\ \hline Accuracy & 78.75 & 42.39 & 49.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the ablation study for evaluating the performance of three distinct ML architectures over the data samples produced with Unity3D. The testing data includes image samples affected by the extreme weather conditions and captured from various angles. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model without Affine** & **Model with Affine** \\
**Transformations, \%** & **Transformations, \%** \\ \hline mAP & 25.8 & 83.8 \\ Average IoU & 46.31 & 78.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results comparing the effect of re-training the ML model on the data modified with affine transformations. The affine transformations include such geometrical vector modifications as rotation, scale modification, shift, and reflection. As one can see, in the case of the 0.5 m distance, the ML model demonstrated high performance in both daylight and nighttime conditions, with an accuracy of 97% and 94%, respectively. However, increasing the distance from the camera to 2 m affected the performance significantly, and even critically, in the nighttime conditions, while, in case of the daytime, the performance was still over 80%. For the nighttime images, it dropped below 50%, which substantially exacerbated the AV's computer-vision-based localization. Further increasing the distance to 5 m completely prevented the ML model from selecting the correct operation in the nighttime. However, for the images captured in the daytime, even though the demonstrated performance was impractical, it was still above the 50% boundary.