Car Insurance Damage Ditection System Using AI And Deep Learning

Abstract

The increasing number of road accidents has significantly raised the demand for efficient and accurate car insurance claim processing systems. Traditional damage inspection methods rely heavily on manual assessment by surveyors, which can be time-consuming, costly, and prone to human error. This project proposes an intelligent Car Insurance Damage Detection System that leverages Artificial Intelligence (AI) and Machine Learning (ML) techniques to automatically identify and classify vehicle damage from images. The system utilizes Computer Vision algorithms and Deep Learning models such as Convolutional Neural Networks (CNN), YOLO (You Only Look Once), and ResNet for detecting damaged regions including dents, scratches, and broken parts. Image preprocessing techniques such as normalization, resizing, and feature extraction are applied to improve detection accuracy. The backend of the system is developed using Python with frameworks like TensorFlow, PyTorch, and OpenCV for model training and inference, while the frontend interface is implemented using React.js and the server-side logic is handled with Node.js and Express.js. A trained dataset of car damage images is used to train the model, enabling automated damage assessment and preliminary cost estimation. Additionally, Natural Language Processing (NLP) language models can be integrated to generate automated claim reports and assist customer communication. The proposed system improves the efficiency of the insurance claim process by providing fast, reliable, and automated damage analysis, reducing manual effort and minimizing fraudulent claims. This solution demonstrates how AI-driven automation can enhance the digital transformation of the insurance industry.