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Potholes are among the most common forms of road-surface deterioration, and their presence can compromise vehicle safety, ride comfort, and the long-term condition of transportation infrastructure. Conventional inspection practice still relies mainly on manual surveys carried out by trained personnel or dedicated survey vehicles, an approach that is slow, resource-intensive, and difficult to sustain across large road networks. This paper proposes a computer-vision-based framework for automatic, real-time pothole detection from road images and video streams, built around a deep-learning object detector that localizes potholes with bounding boxes and associated confidence scores. The framework is designed around the Road Damage Dataset 2022 (RDD2022), with image preprocessing and data-augmentation strategies intended to make the detector more robust to variation in road surface, lighting, and weather. Evaluation is planned using standard object-detection metrics, namely precision, recall, F1-score, mean Average Precision (mAP), inference time, and frames per second (FPS), and the system is designed for deployment on a vehicle-mounted camera to enable continuous road-condition monitoring. Reported benchmarks from recent YOLO-based road-damage and pothole-detection studies are also reviewed to situate the expected performance envelope of the proposed design. Taken together, the work sets out a practical route toward an automated, scalable alternative to manual pothole surveys, aimed at supporting faster and better-informed road-maintenance decisions.