README.md

metadata

title: Wheel_Defect_Detection
emoji: 🛞
colorFrom: red
colorTo: red
sdk: streamlit
app_port: 8501
tags:
  - streamlit
app_file: app.py
pinned: false
short_description: Streamlit template space
license: mit
sdk_version: 1.45.1

Welcome to Streamlit!

Edit /src/streamlit_app.py to customize this app to your heart's desire. :heart:

If you have any questions, checkout our documentation and community forums.

🛞 Tire Defect Detection using YOLOv8 A real-time deep learning project to detect and classify tire defects such as bulges, cracks, and flat spots using the YOLOv8 object detection model.

🔍 Objective The goal of this project is to overcome the limitations of traditional sensor-based tire defect detection systems (like in the research paper) by using a camera-based, AI-powered solution that:

Works in real-time Requires no specialized hardware Supports multiple defect types 🚀 Features Detects 4 classes: Bulge, Cracks, Flat Spots, Non-defective Trained using YOLOv8n (Ultralytics) Works with static images and can be extended to video/webcam Real-time feedback with bounding boxes Easy deployment and portable 📂 Dataset Labeled dataset from Roboflow in YOLO format Classes: ['Bulge', 'Cracks', 'Flat spots', 'Non-defective']