Intelligent Car Damage Detection System

This research develops an automated car damage detection system using computer vision and deep learning algorithms to detect and classify damage on cars. Traditional methods rely on human inspectors, which are time-consuming and costly. The proposed system uses convolutional neural networks and transfer learning on VGG-16 for efficient and accurate detection. The developed pipeline breaks down the process into distinct stages for maximized efficiency and accuracy. This allows for more efficient and informed decision-making by insurance companies, maintenance providers, or individuals. The system provides a more efficient, accurate, and cost-effective way to assess car damages, enhancing road safety with more objective and consistent assessments.