The Authors introduce an innovative method for detecting potholes in road infrastructure through the application of computer vision techniques.Potholes pose a significant challenge to transportation systems and their timely detection is crucial for efficient road maintenance. Existing studies have explored the utilization of computer vision algorithms for image analysis and object recognition to automate the detection process. In this Study, we introduce a robust pothole detection system based on deep learning techniques. Specifically, we employ a ConvNet, YOLOv3, to develop an accurate real-time pothole detection model. Our system demonstrates encouraging outcomes in terms of precision and efficiency, thus enabling cost-efficient and timely road maintenance operations.The proposed solution has the potential to enhance transportation infrastructure management by enabling proactive maintenance measures and ensuring smoother and safer roads for commuters.