International Journal of Science, Engineering and TechnologyAuthors: Professor N.K.Gawade, Anushka Jadhav, Aditi Kumari, Yugandhara Topalmode, Pallavi Khamkar
Abstract: Road surface monitoring is a critical component in ensuring transportation safety and reducing vehicle damage. This paper presents the implementation and performance evaluation of two deep learning-based object detection models, YOLOv8 and Single Shot Detector (SSD), for real-time pothole detection. The proposed system is capable of processing both static images and live video streams, enabling automated road inspection. A custom dataset of road images captured under varying environmental conditions was used for training and testing the models. Experimental results demonstrate that YOLOv8 significantly outperforms SSD in both accuracy and real-time detection capability. YOLOv8 achieved an accuracy of 95%, whereas SSD attained 80% accuracy. Furthermore, during real-time video testing, YOLOv8 exhibited superior detection consistency by identifying a higher number of potholes with fewer missed detections compared to SSD.
