PDS-UAV: A Deep Learning-Based Pothole Detection System Using Unmanned Aerial Vehicle Images

Smart cities utilize advanced technologies to enhance quality of life by improving urban services, infrastructure, and environmental sustainability. Effective pothole detection and repair strategies are essential for improving quality of life as they directly impact the comfort and safety of road users. In addition to causing serious harm to residents' lives, potholes can also cause costly vehicle damage. In this study, a pothole detection system utilizing unmanned aerial vehicles, called PDS-UAV, is developed. The system aids in automatically detecting potholes using deep learning techniques and managing their status and repairs. In addition, it allows road users to view an overlay of the detected potholes on the maps based on their selected route, enabling them to avoid the potholes and increase their safety on the roads. Two data collection methods were used, an interview and a questionnaire, to gather data from the target system users. Based on the data analysis, the system's requirements, design, and implementation were completed. For the pothole detection, a deep learning model using YOLOv8 was developed, which achieved an overall performance of 95%, 98%, and 92% for F1 score, precision, and recall, respectively. Different types of testing has been performed on the target users to ensure the system's validity, effectiveness, and ease of use, including unit testing, integration testing, and usability testing. As a future work, more features will be added to the system in addition to improving the deep learning model accuracy.