Real-Time Monocrystalline Solar Cell Defect Detection & Tracking using YOLOv11 + OpenCV ⚡️🔍
🚀 New Project Release: Real-Time Monocrystalline Solar Cell Defect Detection & Tracking using YOLOv11 + OpenCV ⚡️🔍
Excited to share my latest Computer Vision project where I built a real-time solar cell defect detection system powered by:
🧠 YOLOv11 for object detection
🎯 Custom tracking with ID management
📹 Live video analysis with OpenCV
🖼️ Dynamic overlays showing FPS, cell counts, and defect alerts
📝 CSV logging + defect image saving
✅ Modular Python architecture with logo & branding support
🧪 This tool helps identify defective monocrystalline solar cells on-the-fly, even from video footage — ideal for drone-based inspections in solar farms! 🌞📡
💡 Key Features:
✅ Class-wise counters for "good" and "defected" cells
✅ Instant snapshot of new defects
✅ Semi-transparent HUD-style overlay
✅ Real-time performance and frame tracking
✅ Exportable detection logs & defect crops
✅ Developer credits watermark (yes, with emojis!) 😎
🔗 Codebase is modular and clean — ready to plug into industrial pipelines or edge AI setups.
Special thanks to the open-source community and Ultralytics for pushing YOLO to the next level! 🙏
🔧 Tools: YOLOv11, OpenCV, Pillow, Python3.10+