An intelligent approach for automated vehicle damage classification

Manual examination using imitative assessment methods requires considerable investment in time, effort, resources, and funds. Furthermore, these assessments often exhibit inconsistencies. Therefore, this study introduces a framework for accurately detecting and classifying vehicle damage and estimating repair costs using deep learning and machine learning approaches. In this study, YOLOv5 and YOLOv8 models were applied to detect and classify vehicle damage types, such as broken lamps, glass shatters, cracks, scratches, and dents. Repair costs were predicted using the XGBoost machine learning model. The dataset used for damage detection, classification, and cost prediction was created from scratch. It was developed by identifying the types of damage and generating bounding box coordinates around damaged areas. After extracting the bounding box coordinates and damage types, additional features were incorporated. The YOLOv8 model outperformed YOLOv5 in both detecting and classifying vehicle damage, achieving a precision of 87% on the validation dataset and mean absolute precision 90.7% on the test dataset. The XGBoost model achieved an R² score of 97.28% and a Mean Absolute Error (MAE) of 128.50. These results confirm that the proposed framework enhances the precision of vehicle damage detection and classification while accelerating assessment and ensuring its effectiveness in real-world scenarios.