Abstract

The paper presents an intelligent system for automatic vehicle damage assessment using deep learning and computer vision techniques. Traditional insurance claim processes often rely on manual inspections, which are time-consuming, subjective, and error-prone. To overcome these limitations, a Convolutional Neural Network (CNN) model is trained to classify vehicle damage severity into three categories: Minor, Moderate, and Severe. The model is integrated into a Flask-based web application that enables users to upload images, receive real-time predictions, and obtain repair cost estimates along with insurance recommendations. The system demonstrates high accuracy and reliability, offering a scalable solution for insurance automation, improving efficiency, consistency, and decision-making in vehicle damage evaluation.

References

• Zhao, Y., Xu, Y., & Wang, L. (2023). "A Deep Learning Approach for Vehicle Damage Detection and Severity Classification" IEEE Transactions on Intelligent Transportation Systems.
• Liu, S., Chen, X., & Zhang, Q. (2022)."Real-Time Car Accident Detection and Insurance Assessment Using YOLO and CNN"
• Applied Sciences, MDPI.
• Kumar, R., & Pandey, D. (2021)"Automated Damage Detection System for Motor Insurance Claims Using Deep Learning Models" International Journal of Computer Applications (IJCA).
• Rathore, M. M., Paul, A., & Huang, B. (2020). "Vision-Based Insurance Claim Processing Using AI" Sensors, 20(12), 3516.
• TensorFlow Documentation (2024) "TensorFlow: Open-Source Platform for Machine Learning."
• Keras Documentation (2024) "Keras API Reference for Building Deep Learning Models."
• OpenCV Documentation (2024) "OpenCV: Open Source Computer Vision Library for Image Processing."
• Flask Documentation (2024) "Flask: Lightweight Web Framework for Python."
• Kaggle Vehicle Damage Dataset (2022) "Vehicle Damage Image Dataset for Deep Learning Classification"
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). "Deep Learning." MIT Press.
• Pytorch vs TensorFlow: Comparative Study in Automotive Applications (2023) "Performance Evaluation of PyTorch and TensorFlow for Vehicle Damage Prediction" Journal of AI Research and Applications.
• Wang, J., Zhou, K., & Li, M. (2021). "A Multi-Stage Deep Learning Pipeline for Car Damage Detection and Localization."
• Pattern Recognition Letters, Elsevier.
• Al-Rahhal, M. M., & Al-Zawqari, S. (2020). "Image-Based Vehicle Damage Assessment Using Mobile Deep Learning Frameworks." Procedia Computer Science, 170, 708–713.
• Sharma, S., Gupta, A., & Verma, N. (2023). "YOLOv5-Based Real-Time Vehicle Damage Detection and Classification for Insurance Automation." International Journal of Artificial Intelligence and Applications.
• Kiran, B. R., Thomas, M., & Varghese, D. (2022). "Application of Transfer Learning for Automotive Damage Severity Classification." Materials Today: Proceedings (Elsevier).
• Mahajan, A., & Kulkarni, S. (2021). "Smart Claim Settlement System for Vehicles Using AI and Blockchain." International Research Journal of Engineering and Technology (IRJET).
• Chaudhary, R., & Yadav, D. (2023). "Review on Deep Learning Models for Object Detection and Damage Classification in Automobiles." Journal of Intelligent Systems and Internet of Things.
• Raj, A., & Mehta, P. (2020). "Automated Damage Detection in Vehicles Using CNN and Image Segmentation." International Journal of Innovative Research in Computer and Communication Engineering.
• Fang, H., & Kim, H. (2022). "Real-Time Vehicle Damage Detection Using Edge AI and Lightweight CNN Models." Sensors, 22(8), 2921.
• Tripathi, K., & Rani, P. (2024). "AI-Driven Vehicle Condition Evaluation for Insurance Using Vision Transformers." IEEE Access.
• X. Zhang, Y. Li, and Z. Wang, “Deep Learning-Based Vehicle Damage Detection and Classification,” International Journal of Computer Vision Applications, vol. 12, no. 3, pp. 145–154, 2020.
• R. Kumar, P. Singh, and S. Gupta, “Automated Vehicle Damage Assessment Using Computer Vision,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 210–218, 2021.
• L. Chen and H. Wang, “A Comprehensive Study on Vehicle Damage Detection Using Deep Learning,” Pattern Recognition Letters, vol. 156, pp. 45–53, 2022.
• A. Sharma and K. Verma, “Vehicle Damage Detection Using YOLOv7 and Image Augmentation Techniques,” Journal of Artificial Intelligence Research and Applications, vol. 8, no. 2, pp. 77–86, 2023.