AI-based defect detection system that uses computer vision to automatically identify and classify product defects in manufacturing lines — reducing manual inspection time and catching defects the human eye misses.
- 📷 Computer vision — automatically detects defects from product images
- 🏷️ Classification — categorizes defects by type and severity
- 🏭 Manufacturing-ready — designed for production-line quality control
- ⚡ Real-time — built for inline inspection workflows
| Industry | Example Defects Detected |
|---|---|
| Automotive | Scratches, dents, paint defects |
| Electronics | Solder defects, component misalignment |
| Textiles | Weaving flaws, stains |
| Packaging | Print defects, seal failures |
git clone https://github.com/deva2006-raut/Defect-Detacion.git
cd Defect-Detacion📌 Note: This repository currently hosts the project documentation and planning materials. Full implementation coming soon — watch this space! ⭐
- Dataset collection & annotation
- Model training (object detection)
- Real-time inference pipeline
- Web dashboard for QC operators
- Deployment guide for factory hardware
MIT © 2026 Devanshu Raut — see LICENSE