A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning
A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning | Python Final Year IEEE Machine Learning Project 2024 - 2025.
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🔗Email: [email protected],
🌐Website: https://www.jpinfotech.org
📌Our Proposed Project Title: Smart Car Damage Detection and Analysis Using Deep Learning with YoloV8.
💡Implementation: Python.
🔬Algorithm / Model Used: YOLOv8 Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/
📘Project Abstract:
This project presents an advanced system for Automated Vehicle Damage Localization and Severity Estimation using deep learning, developed to automate the traditionally manual process of assessing vehicle damage. Built using Python for the backend and HTML, CSS, and JavaScript for the frontend, the system operates on the Flask web framework, offering a user-friendly, web-based interface. At the core of this system is the YoloV8 model, a state-of-the-art object detection algorithm capable of identifying and categorizing vehicle damage with high precision. The system achieves an accuracy of 91%, making it an effective solution for tasks like insurance claims processing, vehicle repair evaluation, and accident analysis.
The system supports three modes of operation: image-based, video-based, and webcam-based predictions, making it versatile for various practical applications. In the image-based mode, users upload images of vehicles, and the model analyzes the image to detect, localize, and classify different types of damage. The video-based mode extends this capability to moving images, enabling real-time damage detection in videos. Finally, the webcam-based mode provides a live stream for instant damage detection, making the system adaptable to dynamic environments like car repair shops or roadside assistance services.
📌IEEE Project Title: A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning.
📌REFERENCE:
Yuntao Ma , Hiva Ghanbari, Tianyuan Huang, Jeremy Irvin, Oliver Brady, Sofian Zalouk, Hao Sheng, Andrew Ng, Ram Rajagopal , Member, IEEE, and Mayur Narsude, “A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 25, NO. 6, JUNE 2024.
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