AI-Based Visual Defect Detection System | YOLOv8 + OpenCV + Roboflow with Source Code
AI defect detection app with YOLOv8, OpenCV, and React; real-time visual inspection, analytics dashboard, PDF export, secure login, responsive UI, PostgreSQL database.
The AI-Based Visual Defect Detection System is a cutting-edge web-based platform engineered for the automated detection and analysis of surface-level defects in industrial components. Leveraging the state-of-the-art YOLOv8 deep learning model, robust OpenCV image processing, and integration with Roboflow, the system delivers high-precision, real-time defect detection and fallback analysis. The dashboard offers interactive analytics, upload management, and instant PDF reporting. Designed for industrial prototyping, research, and advanced academic learning, this solution features a scalable PostgreSQL database with Drizzle ORM and a fully responsive UI using React.js and Tailwind CSS. The backend is powered by Node.js, Express, and Python for seamless communication with the AI models. Key functionalities include live image analysis, historical results, in-depth defect accuracy metrics, and comprehensive export options. Future scope covers multi-class defect handling, IoT device integration, extended cross-platform support, and advanced analytics modules for manufacturing and quality control environments.
Features and Functionalities
- Real-time defect detection using YOLOv8 (Roboflow integration)
- Automated fallback analysis with OpenCV image processing
- Dashboard for uploads, analytics, and historical analysis
- PDF report generation and export capabilities
- PostgreSQL database with Drizzle ORM for robust data management
- Responsive UI built with React.js and Tailwind CSS
- RESTful architecture: React + Node.js + Express | Python + OpenCV + YOLOv8 | PostgreSQL
- User authentication, session management, and secure data handling
- Notification and alert system for analysis results
- Analytics on detection accuracy, processed images, and detected defect counts
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