earticle

영어

Road infrastructure is a key facility that connects economy, logistics, and life, and maintenance is essential for traffic safety. Damage such as potholes and cracks increases accidents and costs, related complaints and damages are rapidly increasing in Korea. To solve this problem, this study proposed a model that automatically detects road damage such as potholes and cracks using the YOLOv8n model for real-time detection. The experiment was conducted based on Roboflow's RoadDamages Detection dataset and the collected dataset. The proposed model was designed with the aim of high precision to Detection of Road Damage(DRD) in real-time, and Accuracy 0.83 and FPS 25 per second were achieved through data augmentation and optimized hyperparameters. By utilizing this, it can increase road maintenance efficiency, contribute to automation of road management and cost reduction, reduce traffic accidents, and strengthen in terms of traffic safety and economy. In addition, it can be expected to be used in various fields such as traffic infrastructure management. In the future, it plans to improve detection accuracy and speed through various backbone integration, data expansion, and hardware optimization.