Describe your project

AI-Powered Fabric Defect Detection: Automating Textile Quality Control
Our AI-powered solution automates textile quality control using deep learning and computer vision, offering quick and accurate defect detection in fabrics.
In-Scope Features:

Image Upload: Users can upload fabric images for automated analysis.
Defect Detection: A pre-trained MobileNetV2 model detects fabric defects.
Visualization: Grad-CAM heatmaps visually highlight potential defect areas.
Binary Classification: The system classifies images as either "Defect Detected" or "No Defect Detected."
Confidence Score: The model provides a confidence score for the prediction.
Explanation: Results are explained based on the heatmap outputs.
Contextual Analysis: The system suggests potential causes of defects.
Recommendations: Provides generic suggestions for addressing the detected defects.
User Interface: A web interface built using Streamlit makes it easy to use.
Out of Scope:

Multi-class Defect Classification: Does not identify specific defect types.
Real-time Processing: Not designed for production line analysis.
Manufacturing System Integration: This is a standalone solution with no system integration.
Custom Model Training: Uses a pre-trained model without textile-specific training data.
Historical Data Tracking: No storage or analysis of past defect data.
Automated Reporting: No comprehensive report generation.
Mobile Application: Limited to a web-based interface.
Future Opportunities:

Multi-class Classification: Identify specific fabric defect types.
Custom Dataset: Train the model on textile-specific data to improve accuracy.
Real-time Analysis: Integrate camera feeds for production line analysis.
Integration: Develop APIs for manufacturing and ERP system integration.
Advanced Reporting: Provide detailed analytics and reports for quality control.
Continuous Learning: Implement a feedback loop to continuously improve the model.
Mobile App: Develop a mobile application for on-the-go inspections

Challenges we ran into

During the development of our AI-Powered Fabric Defect Detection system, we faced several hurdles:

1. Model Adaptation:
   Adapting the pre-trained MobileNetV2 for fabric defect detection required modifying its architecture. We experimented with different layer configurations and activation functions to optimize for our binary classification task.
2. Grad-CAM Implementation:
   Implementing Grad-CAM for visualization was complex. We ensured correct gradient computation and heatmap generation aligned with our model architecture, studying research papers and open-source implementations.
3. Image Preprocessing:
   Creating a consistent preprocessing pipeline for varied fabric images (texture, color, lighting) was crucial. We experimented with various techniques to ensure reliable model performance.
4. Streamlit Integration:
   Integrating Streamlit with our TensorFlow model and OpenCV pipeline required careful management of dependencies and data flow. We ensured correct processing and display of uploaded images in the interface.
5. Performance Optimization:
   Real-time model inference and Grad-CAM computation posed challenges, especially for larger images. We implemented efficient resizing and data handling to improve responsiveness.
6. False Positives/Negatives:
   Balancing defect sensitivity while avoiding false positives was challenging. Careful threshold tuning highlighted the need for a future multi-class approach.
7. Contextual Analysis Limitations:
   Providing meaningful analysis based solely on image data proved difficult. Our current generic analysis emphasizes the need for future integration with broader manufacturing data.
   To overcome these challenges, we:
8. Experimented extensively with model architectures and hyperparameters.
9. Studied and adapted Grad-CAM implementations from research.
10. Developed a robust image preprocessing pipeline.
11. Iteratively refined our Streamlit app for optimal performance.
12. Recognized current limitations to inform future enhancements.