About the Project — God’s Eye

Inspiration

While monitoring large-scale infrastructure sites through satellite images for a research project, we noticed how subtle structural or environmental changes often go undetected until they become costly failures. Across industries, early visual warnings such as cracks in infrastructure, equipment wear, product quality deviations, or signage non-compliance often go unnoticed until they escalate into expensive failures. Manual inspections are slow, inconsistent, and difficult to scale. This motivated the idea of God’s Eye, an AI system that continuously monitors the physical world and detects critical visual changes early.

What We Plan to Build

God’s Eye is a Visual Difference Engine that automatically detects, segments, and classifies changes in images captured over time.

It will:

• Compare images from cameras, drones, and satellite feeds
• Detect where a visual change has occurred
• Classify the nature of change such as corrosion, obstruction, decay, or contamination
• Provide change heatmaps and severity scores for rapid decision-making

God’s Eye will act as a real-time visual auditor for infrastructure health, manufacturing inspections, and brand compliance.

How We Will Build It

The system will combine computer vision and deep learning techniques:

1. Image alignment using homography-based registration
2. Feature comparison using a Siamese CNN
3. Pixel-level change segmentation using U-Net
4. Change classification using a ResNet-based model
5. Time-series pattern understanding using LSTM layers

Expected Challenges

• Avoiding false positives caused by lighting variations, seasonal changes, and camera viewpoint shifts
• Limited labeled data for rare failure events
• Maintaining strong generalization across different industries and environments
• Designing evaluation metrics that measure early detection impact, not just accuracy

What We Expect to Learn

We expect to learn how to build scalable visual monitoring systems that understand context, not just pixels. We aim to improve temporal visual reasoning and model interpretability for operational use. Most importantly, we want to explore how AI can predict failures by identifying the earliest visible signs of degradation.

Built With

• and-siamese-cnns-for-deep-learning-and-image-analysis
• colab
• docker
• github
• google-earth
• google-earth-engine-and-colab-for-data-and-training
• lstm
• numpy
• opencv
• pandas
• python
• pytorch
• resnet
• scikit-learn
• streamlit
• u-net
• using-pytorch
• with-streamlit-for-visualization