## 5 Results and Discussion
### Performance of YOLO  
Table 8 shows a comparison of the performance of different YOLO object detection models. As we can see, the latest models (YOLOv6 and YOLOv7) perform better than the older ones (YOLOv3 and YOLOv4) in terms of precision, recall, and mAP across different weather conditions, with improvements observed in the worsened lighting conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Data** \\ **Subset** \\ \end{tabular}  & **Precision**  
YOLOv5 and YOLOv6 models demonstrated improvements over their predecessors, YOLOv3 and YOLOv4. On average, YOLOv5m shows a 4.6% increase in precision, recall, and mAP@.5 compared to YOLOv4. On top of that, YOLOv6 saw about a 3.9% increase in precision, 3.4% in recall, and 4% in mAP@.5 compared to YOLOv5. YOLOv7 achieved the highest results on the evaluated pothole dataset. On average, the precision, recall, and mAP@.5 of 88.3% were reached, and the highest mAP@ [0.5:0.95] of 40.3% was also achieved. When comparing the baseline YOLOv3 model to the latest YOLOv7 model, we can see a substantial improvement in the overall performance. YOLOv7 showed a 10.9%, 12.3%, 11.3%, and 7.3% improvement in precision, recall, mAP@.5, and mAP@ [0.5:0.95] score, respectively, compared to YOLOv3 under clear weather conditions. Adverse weather and reduced light have a natural impact on camera sensing and subsequent object detection performance. As in the previous case of R-CNN models, performance of YOLO models degraded when subjected to more difficult visual conditions. Despite the detection accuracy being continuously improved in clear settings, the performance difference between clear and deteriorated conditions may also widen. The mAP@.5 for rain, sunset, evening, and night subsets decreased, on average, by 28.8%, 26.2%, 32.11%, and 64.5% in comparison to clear conditions. The continuous change in the mAP accuracy throughout the different versions of YOLO models is marked in Table 8. The accuracy of the YOLOv3 model is considered the baseline starting point. The difference in accuracy is calculated considering the current highest value achieved for individual data subsets. As can be seen from Table 8, each new version of YOLO comes with additional improvements in the mAP@.5 measure for every data subset. However, YOLO's detection performance rapidly declines at night. Unlike RCNN models reaching up to 30% of mAP@.5, the detection accuracy of YOLO models does not hit above the 20% level of mAP@.5. A comparison of the overall performance of the models, when the mean value of accuracy across all conditions is calculated, is shown in Table 9. The accuracy of the pothole detection task increases with the newer versions of YOLO architecture. In addition, we compare individual YOLO models from several perspectives, including model size, number of parameters, inference time, and performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Data** & **Precision** & **Recall** & **mAP@.5** & **mAP@** \\  & **Subset** & & & **[0.5:0.95]** & **[0.5:0.95]** \\ \hline \multirow{4}{*}{YOLOv7} & Clear & 88.6\% & 88.0\% & 88.4\% + 0.6 & 40.3\% + 2.7 \\ \cline{2-5}  & Rain & 70.6\% & 60.2\% & 58.1\% + 0.7 & 24.9\% + 2.2 \\ \cline{1-1} \cline{2-5}  & Sunset & 79.1\% & 57.7\% & 61.5\% + 1.6 & 22.8\% + 0.8 \\ \cline{1-1} \cline{2-5}  & Evening & 84.9\% & 56.1\% & 54.8\% + 0.7 & 21.5\% + 0.7 \\ \cline{1-1} \cline{2-5}  & Night & 42.8\% & 19.5\% & 20.4\% + 0.5 & 7.5\% + 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._