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This study presents a real-time road damage detection system utilizing the YOLOv8 algorithm, achieving an accuracy of 80% in identifying various types of road damage such as cracks and potholes. The system demonstrates high precision, recall, and F1 scores, making it an effective tool for proactive infrastructure maintenance and enhancing road safety. With real-time processing capabilities, the model can analyze video datasets at an average rate of 25 frames per second, offering a practical solution for tra

Author

armasuka10

Language

EN