Image Recognition for Product Quality

Skills, Tech Stack, and Libraries

1. Skills: Computer Vision, Image Classification, Feature Extraction, Deep Learning, Model Optimization

2. Tech Stack: Python, AWS

3. Libraries: TensorFlow, Keras, OpenCV, Pandas, NumPy, Matplotlib

   
   

Approach

Objective:

I developed an image recognition system to detect product defects in manufacturing processes. The project aimed to improve quality control by automating defect identification, reducing inspection time, and minimizing defective products reaching customers.

Approach:

1. Data Collection:

   • Collected labeled images of products from manufacturing lines, including defective and non-defective samples.

   • Preprocessed images using OpenCV to standardize dimensions, enhance contrast, and remove noise.

2. Data Augmentation:

   • Applied augmentation techniques such as flipping, rotation, scaling, and brightness adjustments to expand the training dataset and improve model robustness.

3. Model Development:

   • Built a convolutional neural network (CNN) using TensorFlow and Keras for image classification.

   • The model was designed to classify products into categories such as "defective" and "non-defective" based on visual features.

   • Fine-tuned a pre-trained model (e.g., ResNet or MobileNet) for faster convergence and improved accuracy.

4. Feature Extraction and Optimization:

   • Extracted visual features such as edges, textures, and color distributions to enhance defect detection.

   • Optimized the model using techniques like learning rate scheduling and dropout to reduce overfitting.

5. Integration and Automation:

   • Integrated the model into the production line using an API deployed on AWS, enabling real-time defect detection.

   • Triggered alerts when defective products were detected, allowing for immediate removal or re-inspection.

6. Visualization and Reporting:

   • Created dashboards to monitor product defect rates, model performance metrics, and production trends over time.

     
     

Code Flow:

1. Load and preprocess image data using OpenCV and Pandas.

2. Perform data augmentation to expand the dataset and improve model robustness.

3. Train and fine-tune a CNN using TensorFlow/Keras to classify defective and non-defective products.

4. Deploy the model via AWS for real-time integration into manufacturing workflows.

5. Visualize results and defect trends using Matplotlib and generate automated reports.

   
   

Results

The Image Recognition for Product Quality System delivered the following outcomes:

• Improved Quality Control: Achieved a defect detection accuracy of over 95%, significantly reducing the rate of defective products shipped.

• Enhanced Efficiency: Automated defect inspection reduced manual inspection time by 70%, streamlining production workflows.

• Real-Time Alerts: Enabled immediate corrective actions, minimizing downtime caused by defective products.

• Cost Savings: Reduced costs associated with product returns and warranty claims by 25%.

This project demonstrated the integration of computer vision and deep learning in manufacturing, revolutionizing quality control processes and ensuring customer satisfaction.

Git Link

For more information and code, visit the Git link.