## 6 Conclusions  
Computer vision techniques have shown promising results in automating pothole detection, but selecting the best model for deployment can be a challenging task, especially if we consider the detection of potholes under different weather conditions. In this article, we focused on introducing the current state-of-the-art CNNs that are used for pothole detection. The main objective is to compare their performance through experiments and provide information that can be useful for future research in this field. This study evaluated the effectiveness of different computer vision models, including Fast R-CNN, Faster R-CNN, Mask R-CNN, Cascade R-CNN, Sparse R-CNN, and YOLO versions 3 to 7, for the task of pothole detection under adverse visual conditions, such as rain, sunset, evening, and night. The models' performances were compared in terms of detection accuracy under different weather and lighting conditions. Our experimental results revealed that YOLOv7, followed by YOLOv6l and YOLOv6m, demonstrated the best performance across all weather conditions. YOLOv5l and YOLOv5m also showed good performance, with slight variations in different weather conditions. These results indicate that YOLO architectures may be the most suitable for pothole detection under adverse visual conditions, such as rain, sunset, and evening. However, it is worth noting that R-CNN models, despite their significant computational costs, proved to be the most suitable for night-time detection. Although YOLO architectures perform detection with significant accuracy and speed, R-CNN models may handle the very low-visibility detection more successfully. The results showed that the performance of the models was negatively affected by lighting conditions, with night data showing the lowest performance. When compared to the clear subset, the mAP@.5 for the night subset decreased, on average, by 41.2% and 64.5% for R-CNN and YOLO models, respectively. These findings highlight the importance of considering different weather and low-light conditions when selecting object detection models. Our study's contributions may provide valuable information for researchers interested in improving pothole detection performance under adverse visual conditions. The proposed study may also contribute to the development of ITS, which aims to improve road safety and reduce the number of accidents caused by potholes. Future research could focus on more diverse and weather-specific data augmentation techniques using generative networks. These methods could enable the generation of synthetic data that accurately captures the complexities of different weather conditions, thus improving the generalization capability of the models. Moreover, model modification such as self-attention modules (e.g., Transformers, CBAM, GAM) for salient feature extraction could be incorporated to improve the detection of relatively small objects in challenging conditions. Novel multi-scale features could also be implemented to capture objects at different scales and enhance model performance. Furthermore, incorporating additional information into the object detection pipeline, such as semantic segmentation or depth estimation, could help to further improve the accuracy of object detection in challenging weather conditions. Additionally, it would be useful to extend the dataset to other weather conditions (snowfall, hail, fog) to assess the robustness of the models. Finally, an interesting direction for future research would be to implement the models on hardware platforms for real-world testing. This could involve deploying the models on drones or vehicles to evaluate their effectiveness in detecting objects in real time, which would have significant implications for applications, such as autonomous driving and aerial surveillance. **Author Contributions:** Conceptualization, M.J., E.L. and B.B. ; methodology, K.Z. ; software, M.J.; validation, B.B., E.L. and K.Z. ; formal analysis, B.B., E.L.; investigation, M.J.; resources, K.Z. ; data curation, B.B.