Industrial Defect Detection System with OpenCV, AI & Cloud Integration

📌 Overview

A Streamlit-powered web application that leverages OpenCV for advanced computer vision tasks, enhanced with AI-driven defect detection and designed for industrial quality control. Integrated with cloud storage options for scalability and secure storage of results and logs.

This system serves educational, research, and industrial purposes, enabling image-based inspection, surface analysis, and dimensional measurement with real-time AI insights.

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🚀 Features

• ✅ Face and eye detection via Haar Cascades
• ✅ Surface defect detection (Cracks, Scratches, Spots, Line Defects)
• ✅ Dimensional measurement using pixel-to-mm calibration
• ✅ Surface texture and roughness analysis
• ✅ Cloud integration for storing images and logs
• ✅ Downloadable processed images
• ✅ Real-time image annotations and metadata

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🛠️ System Architecture

📌 Frontend

• Framework: Streamlit SPA (Single Page App)
• UI: Sidebar controls for selecting operations
• State Management: Streamlit Session State

📌 Backend

• Core Engine: OpenCV (cv2), Numpy, Pillow
• AI Model Support: Pre-trained Haar cascades + placeholders for custom ML models
• Cloud Storage: (Optionally) Integrate AWS S3, Azure Blob Storage, or Google Cloud Storage
• Logging: Captures metadata and actions into cloud or local storage

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🔄 Data Flow

1. 📤 Upload Image
2. ⚙️ Select Processing Type (Face Detection, Defect Analysis, Measurement)
3. 🔍 Apply Processing with OpenCV & AI Models
4. 🎯 Display Processed Results Side by Side
5. ☁️ Optionally Save Results to Cloud
6. 📥 Download Processed Outputs

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🗂️ Project Structure

├── app.py # Streamlit UI & main control logic ├── cv_processor.py # Core computer vision logic ├── utils.py # Utilities: file handling, conversions ├── models/ # Haar cascades and other models │ ├── haarcascade_frontalface_default.xml │ └── haarcascade_eye.xml ├── cloud/ # Optional: Cloud integration utilities │ ├── s3_utils.py # Example for AWS S3 operations └── requirements.txt # Dependencies

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⚡ Installation & Running Locally

1. Clone Repo

git clone https://github.com/birukG09/Industrial-Defect-Detection-System.git
cd Industrial-Defect-Detection-System
Install Dependencies

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pip install -r requirements.txt
Run the App

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streamlit run app.py
🧩 Key Components
✅ CVProcessor Class
Handles:

Face & eye detection

Edge detection

Surface defect detection

Dimensional analysis

AI-based texture insights

✅ Cloud Utilities (Optional)
To:

Upload processed images and logs to AWS S3 or any cloud provider

Retrieve logs for audits

✅ Utilities
Download model files from the web

Image format conversions (PIL <-> OpenCV)

State management helpers

🔒 Security & Production Considerations
Validate file uploads (formats, size limits)

Cache or persist models on server for efficiency

Monitor cloud API keys & access

Deploy on a VM or cloud service with GPU for intensive processing

✅ Deployment Options
Local: Python + Streamlit (best for demos)

Dockerized Deployment: Containerize for production

Cloud VM: Deploy on AWS, Azure, GCP

Serverless Extensions: Add APIs via FastAPI for integrations

🛠️ External Dependencies
streamlit

opencv-python

numpy

pillow

boto3 (optional for AWS S3)

🔮 Future Enhancements
✅ Integrate Deep Learning Models (YOLO, SSD) for real-time detection

✅ Build REST APIs using FastAPI for scalable backend

✅ Add database logging (MongoDB/PostgreSQL) for industrial logs

✅ Build mobile-friendly UI with Streamlit Components

🧑‍💻 Author
Biruk G | GitHub Profile

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