UAV Model Deployment on NVIDIA Jetson

Real-time UAV detection using TensorRT and MNN on edge devices with Docker-based deployment pipeline

Overview

This project focuses on deploying deep learning models for UAV (Unmanned Aerial Vehicle) detection on NVIDIA Jetson edge devices. The goal is to achieve real-time inference performance while maintaining high detection accuracy under resource-constrained conditions.

Key Technologies

  • TensorRT: NVIDIA’s high-performance deep learning inference optimizer and runtime, used for INT8 quantization and inference acceleration
  • MNN: Alibaba’s lightweight deep learning inference engine, optimized for mobile and edge deployment
  • Docker: Containerized deployment environment ensuring reproducibility across different Jetson devices
  • CUDA: GPU-accelerated computing for parallel inference

Technical Highlights

Model Optimization Pipeline

  1. Model Training: Train detection model on GPU workstation
  2. ONNX Export: Convert trained model to ONNX intermediate representation
  3. TensorRT Optimization: Apply INT8 quantization and layer fusion via TensorRT
  4. MNN Deployment: Alternative lightweight deployment using MNN framework
  5. Docker Packaging: Containerize the entire inference pipeline

Performance Metrics

Metric Before Optimization After TensorRT INT8
Inference Time ~50ms ~12ms
Model Size ~200MB ~50MB
GPU Memory ~1.5GB ~0.5GB

Docker Environment

The deployment pipeline is fully containerized:

# Example Dockerfile structure
FROM nvcr.io/nvidia/l4t-tensorrt:rXX.X.X-runtime
# Install dependencies
# Copy optimized model
# Set up inference server

Future Work

  • Explore model distillation techniques for further compression
  • Implement multi-model ensemble on Jetson Orin
  • Add real-time video stream processing pipeline