Enhancing Safety in Autonomous Vehicles Using Advanced Deep Learning-Based Pothole Detection

Autonomous vehicles possess the potential to revolutionize transportation by significantly enhancing safety and efficiency. However, their success hinges on overcoming numerous challenges including the detection of potholes which pose significant risks to vehicles and passengers. Consequently the identification and remediation of these obstacles are crucial for the safety of autonomous systems. This research introduces YOLO v8 as a formidable solution for pothole detection predicated on the latest You Only Look Once (YOLO) algorithm. Utilizing deep learning techniques this system identifies potholes in real-time enabling autonomous vehicles to circumvent potential hazards and diminish the risk of accidents. Extensive testing with publicly accessible datasets reveal that this approach surpasses contemporary state-of-the-art methodologies in both precision and speed. Various data augmentation strategies are also examined to further enhance detection performance. Empirical evidence indicates that the YOLO v8-based pothole detection system exhibits superior efficacy compared to other analogous systems. This advancement signifies that autonomous driving can be rendered safer and more reliable marking a pivotal milestone in the enhancement of road safety.