Multiverse Computing partnered with a transnational defense and security organization to deliver a sovereign, edge-deployable AI capability for border surveillance over massive geographies. The project demonstrates how heavy optimization of state-of-the-art vision models turns wide-area monitoring into a routine, scalable operation, even on constrained hardware, and gives the client full control over the data, models and inference behind a mission-critical capability.
The Challenge
The client needed to perform real-time object detection over more than 670,000 km² using high-resolution, multi-spectral satellite imagery, including RGB, infrared and synthetic aperture radar. The data volume, the image resolution (30 cm per pixel) and the refresh requirement (multiple inferences per hour per location) placed significant pressure on infrastructure and energy budgets, while the mission context required no compromise on accuracy. Standard object detection models were too heavy and too slow to be viable within the deployment footprint required.
Our Solution
Multiverse Computing applied its proprietary optimization technology to YOLOv8-x, delivering a Multiverse Computing model that is significantly smaller, faster and more efficient, with no loss in accuracy. The compressed model runs at the edge on low-resource hardware, enabling scalable, high-frequency satellite image processing with a fraction of the original infrastructure and energy footprint.
- Model compression of YOLOv8-x with 0% accuracy loss versus the baseline.
- Edge-ready deployment on resource-constrained satellite and ground hardware.
- High-frequency refresh cycles, with multiple inferences per hour per location, across massive geographies.
- Sovereign architecture with no dependence on large-scale third-party cloud infrastructure.
Reference for the underlying compression methodology: Tensor network compressibility of convolutional models, https://arxiv.org/pdf/2403.14379.
Results
Smaller, faster, more efficient model with no accuracy loss.
Scalable real-time monitoring at the edge.
Sustained coverage of 670K km² with high-frequency refresh.
Reduced reliance on large-scale third-party cloud infrastructure.
Lower energy footprint and operational cost.
Reusable optimization pipeline for additional vision models.
Strategic outcome: a sovereign, edge-deployable border surveillance capability that scales across massive geographies, preserves detection accuracy, and gives the client full control over the AI behind a mission-critical operation.
