Implementing Domain Adaptation to YOLOv5 Algorithm for Pothole Detection

Potholes are a major concern for both road safety and vehicle maintenance, as they not only increase the risk of accidents but also lead to high repair costs. Traditional methods of detecting potholes manually are inefficient, time-consuming, and prone to errors, which has led to the adoption of ad-vanced machine-learning techniques. In this study, we utilize the YOLOv5 algorithm, well-known for its real-time object detection abilities, to tackle the pothole detection problem under various environmental conditions. One of the key challenges addressed is the algorithm's performance variability when exposed to different weather and lighting conditions, which can significantly impact the accuracy of detection. To mitigate these challenges, domain adaptation techniques were employed, allowing the model to adapt effectively to diverse environmental scenarios and thereby improving detection accuracy. The methodology involved training the YOLOv5 model on datasets that simulated a wide range of conditions and subsequently testing its performance on different datasets. The results indicate that domain adaptation greatly enhances the model's precision, with the highest precision metric achieved being 0.968. This study demonstrates that the integration of domain adaptation with advanced object detection algorithms like YOLOv5 can significantly improve pothole de-tection, contributing to safer roads as well as reducing vehicle damage.