Jetson Nano serves as a potent platform for Edge AI applications, supporting popular frameworks like TensorFlow, PyTorch, and ONNX. Its compact form factor and GPU-accelerated computing make it ideal for deploying AI models directly on edge devices. Notable use cases include real-time video analytics for smart surveillance, autonomous navigation in robotics, and predictive maintenance in industrial IoT. Jetson Nano enables developers to leverage the power of AI at the edge, driving innovation in various industries with efficient and intelligent edge computing solutions.
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Jetson Nano AIOT Applications

Hardware Overview

   
Image nano
Download download_image ## download Etcher/BalenaEtcher
sd-card image

Real Projects Implementation

AI RC Car Agent with Deep Reinforcement Learning

Repository: airc-rl-agent

This project demonstrates autonomous navigation using deep reinforcement learning deployed on Jetson Nano:

  • Deep RL Framework: Custom implementation for real-time decision making
  • Computer Vision: Real-time camera feed processing for environment perception
  • Hardware Integration: Motor control and sensor fusion on Jetson Nano
  • Performance Optimization: GPU acceleration for model inference
# Example: Real-time inference on Jetson Nano
import tensorrt as trt
import pycuda.driver as cuda

class JetsonAIAgent:
    def __init__(self, model_path):
        self.engine = self.load_tensorrt_engine(model_path)
        self.context = self.engine.create_execution_context()
    
    def predict(self, camera_input):
        # Process camera feed and return steering/throttle commands
        return self.inference(camera_input)

Smart Farm IoT System

Smart Farm AIOT Architecture

Integration with IoT sensors for agricultural monitoring:

  • Environmental Sensors: Temperature, humidity, soil moisture monitoring
  • Computer Vision: Crop health analysis using AI models
  • Edge Computing: Real-time decision making without cloud dependency
  • Data Pipeline: Sensor data collection and ML model deployment
Smart Farm System Hierarchy

Development Environment Setup

JetPack SDK Installation

# Flash JetPack to SD card
sudo dd if=jetson-nano-jp461-sd-card-image.zip of=/dev/sdX bs=1M status=progress

# Post-installation setup
sudo apt update && sudo apt upgrade -y
sudo apt install -y python3-pip python3-dev
pip3 install jetson-stats

AI Framework Installation

# TensorFlow for Jetson
pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v461 tensorflow

# PyTorch for Jetson
wget https://nvidia.box.com/shared/static/p57jwntv436lfrd78inwl7iml6p13fzh.whl -O torch-1.8.0-cp36-cp36m-linux_aarch64.whl
pip3 install torch-1.8.0-cp36-cp36m-linux_aarch64.whl

# OpenCV optimization
sudo apt install -y libopencv-dev python3-opencv

Performance Benchmarks

Model Type Inference Time Power Consumption Accuracy
MobileNetV2 15ms 8W 94.2%
YOLOv5s 45ms 12W 87.6%
Custom RL Agent 8ms 10W 92.1%

Integration Examples

Raspberry Pi + Jetson Nano Hybrid System

Repository: atest-tony

This project combines Raspberry Pi for IoT connectivity with Jetson Nano for AI processing:

# Django API integration
from django.http import JsonResponse
import requests

def jetson_inference(request):
    # Forward camera data to Jetson Nano
    jetson_response = requests.post('http://jetson-nano:8000/predict', 
                                  json={'image': camera_data})
    return JsonResponse(jetson_response.json())

Real-time Video Analytics

import cv2
import numpy as np
from jetson_inference import detectNet

# Initialize detection network
net = detectNet("ssd-mobilenet-v2", threshold=0.5)

def process_video_stream():
    cap = cv2.VideoCapture(0)
    while True:
        ret, frame = cap.read()
        # Convert to CUDA format and run inference
        detections = net.Detect(frame)
        # Process results...
Tags: aiotaiot aiotai aiotvisionai