CS/02Defense & IoT
Real-Time Defense Vision System
R/0130%Inference SpeedFaster with TensorRT optimization
R/02-25%False PositivesReduction in false positive rates
R/0395%System UptimeProduction system availability
The problem
§01Defense operations required real-time multi-object tracking with minimal latency on edge hardware, plus comprehensive monitoring across 40+ screens. Existing systems had high false positive rates and poor edge performance.
The solution
§02We deployed a computer vision pipeline on NVIDIA Jetson Orin with TensorRT-optimized models for real-time multi-object tracking. Custom classification pipelines reduced false positives by 25%. A 40+ screen monitoring dashboard system provided comprehensive operational awareness.
Architecture
§03Edge AI on NVIDIA Jetson Orin (ARM64). TensorRT and ONNX optimization for inference. CUDA-accelerated processing. Custom monitoring dashboard system with 40+ screens. Real-time alerting via webhooks.
Stack — 7
NVIDIA Jetson·TensorRT·ONNX·OpenCV·CUDA·Python·Qt·
/08Let's build