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This repository contains the implementation of an advanced machine learning system for detecting anomalies in PCB components. The system uses infrared imaging to identify and mark overheated or faulty components, ensuring efficient and accurate fault detection.

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astromanu007/PCB_Anomaly_Detection

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🌡️🔍 PCB Thermal Anomaly Detection System 🔍🌡️

Welcome to the PCB Thermal Anomaly Detection System! This project allows for real-time thermal imaging analysis of printed circuit boards, highlighting hotspots and saving captured images to help streamline manufacturing efficiency by identifying overheated components with precision. 🚀


📖 Features 📖

  • Real-Time Anomaly Detection: Automatically highlights overheating areas on PCBs with visual markers. 🔴🟡
  • Customizable Temperature Thresholds: Adjust and monitor threshold settings for precise control over what qualifies as a ‘hot spot’. 🔥
  • Automatic Snapshot & Storage: Captures and saves images of detected anomalies in a dedicated folder. 🖼️
  • User Interface: A modern, responsive Pygame interface with interactive animations and sleek design. 🖥️✨
  • Accuracy and Intensity Graphs: View live accuracy and thermal intensity metrics for consistent monitoring! 📈📊

🖼️ Screenshots 🖼️

Main Interface

Main Interface

Welcome Screen

Welcome Screen

Real-Time Thermal Video Mode

Real-Time Video Mode

Thermal Tracking Results

Thermal Tracking Results

Detection Accuracy and Intensity Graphs

Graphs


🚀 Getting Started 🚀

🧩 Prerequisites 🧩

  • Python: Ensure Python 3.6+ is installed 🐍.
  • Dependencies:
    • Install required packages via pip:
    • Or you can instal by running the requirements.txt file
      pip install numpy opencv-python pygame matplotlib
  • USB Thermal Camera: Compatible with FLIR or Seek Thermal, connected via USB. 🔌📷

📂 Setup Instructions 📂

  1. Clone the Repository 📂:
    git clone https://github.com/astromanu007/PCB_Anomaly_Detection.git
    cd PCB_Anomaly_Detection

2. Project Structure 📁

PCB_Anomaly_Detection/
├── assets/                     # Folder for static assets like icons and images
│   ├── thermal_icon.png
│   └── creator_photo.jpg
├── sample_images/              # Folder with sample PCB images for uploading
├── anomaly_captures/           # Folder where captured anomalies are stored
├── images/                     # Folder with screenshots for README
│   ├── MAIN.jpg
│   ├── INTRO.png
│   ├── VIDEO_MODE.png
│   ├── LIVE_TRACKING_RESULTS.png
│   └── GRAPHS.jpg
├── main.py                     # Main application code for running the PCB Thermal Anomaly Detection System
└── README.md                   # Project README file with detailed documentation
  1. Run the Application 🚀:
python main.py

🎮 User Interface Controls 🎮

  • 🟢 Start/Stop: Toggle real-time monitoring.
  • 🎚️ Threshold Adjustment: Fine-tune temperature sensitivity.
  • 🔴 Record: Enable image capture for anomalies.
  • 📤 Upload: Manually load a sample PCB image from the sample_images folder.
  • 📊 Results: View the processed output with detected hotspots.
  • 📈 Graphs: Visualize detection accuracy and thermal intensity over time.

🛠️ Project Structure & Details 🛠️

Directory Description 📑
main.py Contains the primary application code for the PCB Thermal Anomaly Detection System.
assets/ Folder for static assets including icons, images, and other visual elements.
sample_images/ Contains sample PCB images for uploading and testing.
anomaly_captures/ Stores all captured images of detected anomalies.
images/ Contains screenshots and icons for the README.

🎮 How to Use 🎮

  • 🟢 Start Monitoring: Launches the thermal camera feed for real-time PCB analysis.
  • 🎚️ Adjust Threshold: Increase or decrease the detection temperature threshold to match your analysis needs.
  • 📸 Automatic Capture: When a hotspot is detected, an image is automatically saved in the anomaly_captures folder with the timestamp.
  • 📊 Graphs & Statistics: Track real-time performance stats with dynamic visualizations.

📞 Contact Information 📞

For any questions, suggestions, or collaboration inquiries, feel free to reach out:

Hope you find this tool useful for improving PCB quality control! 😊✨


📢 License 📢

This project is licensed under the MIT License. 📜 Feel free to use, modify, and distribute it as needed.

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This repository contains the implementation of an advanced machine learning system for detecting anomalies in PCB components. The system uses infrared imaging to identify and mark overheated or faulty components, ensuring efficient and accurate fault detection.

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