DEEP LEARNING BASED VEHICLE DAMAGE ASSESSMENT SYSTEM

Abstract

A Deep learning-based vehicle damage assessment system is an intelligent solution that automates the process of detecting and evaluating vehicle damage from images. The system uses advanced deep learning techniques, particularly convoluational neural Networks (CNNs) and object detection models such as YOLO or Mask R-CNN, to identify damaged areas including dents, scratches, cracks, and broken parts. After analyzing the uploaded vehicle images, the system classifies the type and severity of damage and generates an assessment report. This reduce the need for manual inspection, minimize human error, and speeds yup the insurance claim and repair estimation process. The proposed system provides accurate, reliable, and cost-effectiv e damage assessment, making it beneficial for insurance companies, vehicle owners, and automobiles service centers. By leveraging artificial intelligence and computer vivion, system improves efficiency cocsistent evaluations, and enhances customer satisfaction in the vehicle inspection process. KEYWORDS: Deep learning, Vehicle Damage Assessment, computer vivion, convolutional Neural Networks (CNN), Object Detection, Damage Classification, insurance Claim Automation, Artificial Intelligence (AI).