(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Road potholes are one of the primary causes of traffic accidents, vehicle damage, and congestion, especially in developing areas. The situation becomes more critical during monsoon seasons when puddled water completely hides potholes from visual observation. Conventional methods for detecting potholes depend on either manual inspection or basic image processing techniques which require lengthy time periods for their implementation and produce less precise results which cannot be used in expansive operational settings. The research introduces SMARTVISION which functions as a hybrid system for detecting potholes under both dry and flooded road conditions. A vehicle-mounted camera captures real-time road images which the system processes using the YOLOv8 deep learning model to detect potholes through visual features that include edges and texture and irregular shapes. The system uses underwater SONAR sensors to measure road depth changes through ultrasonic wave reflection principles because visual detection has become unreliable in flooded environments. The proposed system achieves better detection performance through its combination of computer vision technology and sensor-based depth analysis which works across different environ- mental conditions. The experimental results show that the system achieves better accuracy and real-time performance and stronger performance than traditional methods. The system enhances transportation safety while it helps develop intelligent transportation systems and smart city infrastructure development