Deep-Learning-AOI-for-PCBs-Real-Time-YOLO-Detection-of-Missing-Holes-Mouse-Bites-Shorts-and-Spurs - GitLite

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Deep-Learning-AOI-for-PCBs-Real-Time-YOLO-Detection-of-Missing-Holes-Mouse-Bites-Shorts-and-Spurs

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⚽ Deep ⚾ Learning 🥎 AOI PCBs is 🏀 an Automated 🏈 Optical 🎳 Inspection ⛸ system Printed 🎮 Circuit Boards ✈ using Deep 🚀 Learning 🚁 YOLO It enables 🚢 real time 🛸 defect detection 🛥 of critical PCB 🚈 issues like 🚒 missing holes 🚞 mouse bites 🏦 shorts and 🧱 spurs improving 🫑 manufacturing 🍅 accuracy reducing 🍏 human inspection errors

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✨ Key Features

🔎 Real-Time YOLO Detection – Detects PCB defects instantly during inspection

🕳️ Defect Categories – Identifies missing holes, mouse bites, shorts, and spurs

🖼️ Image Preprocessing – Data augmentation, resizing, normalization for better accuracy

🤖 Transfer Learning – Fine-tuned YOLO models for PCB-specific datasets

📊 Model Evaluation – Precision, Recall, mAP, confusion matrix for defect classification

⚡ High-Speed Inference – Optimized for real-time industrial AOI workflows

🛠️ Scalable Integration – Can be embedded into PCB assembly lines with cameras

📈 Visualization Tools – Bounding boxes, defect heatmaps, and confidence scores

🧰 Tech Stack

Programming: Python 🐍

Deep Learning Frameworks: YOLOv5 / YOLOv7 / YOLOv8, PyTorch, TensorFlow

Computer Vision: OpenCV, Albumentations

Data Science Tools: NumPy, Pandas, Matplotlib, Seaborn

Deployment: ONNX Runtime, TensorRT, Flask/FastAPI for API integration

📁 Project Structure
📁 dataset/          # PCB defect dataset (train/val/test)
📁 notebooks/        # Jupyter notebooks for model training & evaluation
📁 models/           # Trained YOLO models & weights
📁 src/              # Scripts for preprocessing, training, detection
📁 results/          # Detection outputs, metrics, graphs
📁 deployment/       # Code for real-time inference & API integration

🚀 Getting Started
git clone https://github.com/yourusername/Deep-Learning-AOI-for-PCBs-Real-Time-YOLO-Detection-of-Missing-Holes-Mouse-Bites-Shorts-and-Spurs.git
cd Deep-Learning-AOI-for-PCBs...
pip install -r requirements.txt
python detect.py --source test_images/

📌 Use Cases

🏭 Electronics Manufacturing – Automated PCB inspection on production lines

🔧 Quality Control – Reduce human errors in PCB defect identification

📊 Research & Development – Benchmarking PCB inspection using deep learning

🎓 Educational Projects – Learning deep learning applications in industrial AI

🤝 Contributing

Contributions are welcome! Add datasets, improve detection accuracy, or optimize deployment and submit a PR.

📜 License

MIT License – Open-source for industrial AI innovation and research.

⭐ Support

If this project helps you, please star ⭐ the repo and share it with the AI + Electronics community.