Car defect detection
This project focuses on building an AI-powered car defect detection system that identifies damages such as dents, scratches, glass breakage, and headlight damage using computer vision techniques. It leverages deep learning models like YOLOv8 to train on annotated datasets and accurately detect defects in vehicle images. The system processes input images and outputs defect classification along with bounding box localization for precise identification. Additionally, it incorporates image comparison techniques between clean and damaged car images to enhance detection accuracy and reliability. This solution aims to automate vehicle inspection workflows, improve operational efficiency, and reduce manual errors, making it highly relevant for real-world automotive and insurance applications.