Integration of YOLOv8 and ResNet-50 to Improve Road Damage Detection Performance - Rendi Andrea Pramana; M. Arief Soeleman (JURNAL TEKNIK INFORMATIKA, 2026)
Automatic road damage detection is an important solution for more effective and efficient transportation infrastructure maintenance. This study proposes the implementation of the You Only Look Once version 8 (YOLOv8) method with ResNet50 as a backbone to improve feature extraction capabilities in detecting various types of road damage. The model was trained using a road damage image dataset that has gone through preprocessing and data augmentation stages to enrich image variations. Test results show that the proposed model is able to achieve excellent performance, with an accuracy value of 95.2%, a precision of 0.979, a recall of 0.968, and an F1-score of 0.974. This achievement proves that the integration of YOLOv8 with ResNet50 as a backbone can improve the reliability of the road damage detection system compared to the original model. With this performance, this method has the potential to be applied in a real-time road monitoring system to support more optimal transportation infrastructure maintenance planning.