Potholes on roads affect traffic safety and the overall quality of infrastructure. If left unrepaired, they can lead to increased maintenance costs and broader community impacts. Traditional inspection methods, such as visual surveys by human observers, still have limitations in terms of efficiency, accuracy, and safety. This study introduces a practical implementation combining deep learning and image processing techniques for pothole detection and dimension estimation, with additional road images collected from Thailand. The method employs the YOLOv8n-seg model, which performs instance segmentation to outline pothole boundaries. Training was conducted using a combination of open-source data and a small set of images captured from Thai roads to enhance contextual relevance. Inverse Perspective Mapping (IPM) was applied to estimate pothole dimensions and convert front-view images into bird's-eye views. The segmentation masks predicted by the model were then used to calculate the real size of each pothole. The results highlight the potential of integrating deep learning with image processing techniques to support road condition monitoring in terms of detection and damage assessment.