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Edge AI · Advanced

Real-time object detection on Jetson

Deploy a YOLO model with TensorRT acceleration on Jetson Orin Nano and run object detection locally, without sending camera frames to the cloud.

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Project blueprint

What you'll build

This project focuses on the deployment work that turns a trained vision model into a responsive edge application: model conversion, TensorRT optimization, camera capture, and an inference loop running on Jetson.

You will leave with a clear mental model of where latency comes from and how to measure it on the target device.

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Included build referenceBuild reference — Jetson, camera input, and accelerated inference
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Project overview

Understand the build here. Learn and access the assets in the LMS.

This page covers what the project does, what you need, and how the build comes together. The LMS contains the complete lessons, source code, circuit diagrams, and deployment materials.

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Before you start

Build requirements

01Jetson Orin Nano
02CSI or USB camera
03JetPack and TensorRT runtime
04Basic Python familiarity
From setup to working system

Build sequence

Follow the steps in order, verify each checkpoint, and finish with a working project you can explain and extend.

01

Prepare the Jetson runtime

Set up JetPack, dependencies, camera access, and a reproducible environment.

02

Convert the detection model

Export the model and build an optimized TensorRT engine.

03

Run the camera inference loop

Capture frames, preprocess inputs, and render detections locally.

04

Measure and tune performance

Profile latency, throughput, memory use, and detection confidence.

05

Package the edge application

Turn the working experiment into a repeatable deployment bundle.

Build the next checkpoint

Stop collecting tutorials. Ship a working build.

Use this project as a concrete piece of your embedded, IoT, or edge AI portfolio.

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