The goal of this project is to develop a pothole detection system using image processing method and the YOLOv7 algorithm. The aim is to eliminate mismatching points in pothole detection and attain accurate matching points. Potholes are a common problem on roads and highways and can cause damage to vehicles, as well as pose a safety hazard to drivers and passengers. The use of image processing techniques for pothole detection offers a range of benefits, including improved road safety, cost savings, and improved accuracy and reliability. These benefits have motivated the development and use of these techniques in the field of road maintenance. The proposed system will use YOLOv7, a state-of-the-art object detection algorithm, to detect potholes in images taken by cameras. The algorithm uses a deep neural network to extract features from the images and detect potholes. The system will be trained on a dataset of images of roads with potholes and without potholes. The results of the experiments and evaluations will be presented in a detailed manner. In future work, the system can be improved by incorporating other image processing techniques, such as machine learning, deep learning, and computer vision to increase the accuracy and reliability of the pothole detection system. Additionally, the system can be integrated with mapping systems to provide real-time information about potholes on the roads and highways. In this project, it has achieved significant success in accurately detecting pothole images using the YOLOv7 algorithm, achieving a confidence score of 0.85. Overall, the proposed pothole detection system using image processing techniques and YOLOv7 algorithm has the potential to improve road safety, reduce the cost of road maintenance, and enhance the accuracy and reliability of pothole detection.