The Intelligent Vehicle Damage Detection and Cost Estimation

Abstract

The increasing number of vehicles and road accidents has created a significant demand for fast, accurate, and automated vehicle damage assessment systems. Traditional manual inspection methods are time-consuming, subjective, and often lead to delays in insurance claim processing. This project presents an Intelligent Vehicle Damage Detection and Cost Estimation system that utilizes Artificial Intelligence (AI), Deep Learning, and Computer Vision techniques to automate the damage assessment process.

The proposed system analyzes vehicle images to detect damaged regions, classify the type and severity of damage, and estimate repair costs based on predefined pricing standards. Advanced models such as Convolutional Neural Networks (CNNs) and object detection algorithms are used to improve detection accuracy and efficiency. The system reduces manual effort, minimizes human errors, and accelerates insurance claim processing. Overall, the project demonstrates how intelligent automation can enhance transparency, reliability, and productivity in vehicle damage assessment and insurance management.