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CNN-based Vehicle Damage Detection and Insurance Evaluation using Computer Vision Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 112 |
| Year of Publication: 2026 |
| Authors: Sayyada Fahmeeda, Sadiya Ansari |
10.5120/ijca348a788a5ddf
|
Sayyada Fahmeeda, Sadiya Ansari . CNN-based Vehicle Damage Detection and Insurance Evaluation using Computer Vision Techniques. International Journal of Computer Applications. 187, 112 ( Jun 2026), 24-32. DOI=10.5120/ijca348a788a5ddf
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.
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