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Edge Object Detection

This repository shows you how to do object detection on edge devices and route the detection results to the cloud! The repository includes pipelines that fully automate the azure resource deployments as well as the deployment of edge modules to do the object detection.

This repository uses an example use case of detecting a truck driving on a highway, but by providing your own video (or live feed) you can detect whatever you please!

The Getting Started section of this document will walk you through how to set this up in your environment

Sections

Getting Started

This section will walk through how to get started with your own edge object detection!

Prerequisites

In order to deploy this project you will need the following:

  1. Your own Azure Dev Ops instance to deploy everything to.

    How to create a dev ops instance

  2. An Azure subscription you can deploy resources to.

    Sign up for a free Azure subscription

  3. The replaceTokens Azure Pipelines task

    Some of the IoT Edge Modules pipelines require an Azure DevOps task named replaceTokens that doesn't yet come default in Azure Dev Ops so it must be installed manually.

DevOps Setup

In order to deploy this project and do object detection on the edge in your own resource group you will need to clone this repo into your own Azure Dev Ops instance and take a few more steps.

You will need to do the following steps to get your Azure DevOps instance ready to deploy.

  1. Create an Azure service principal

    az ad sp create-for-rbac --name <ServicePrincipalName>

    Write down and remember it's ID and secret, you will need it later in these steps.

  2. In vars-common.yml update the values of:

    • SUBSCRIPTION_ID to be your Azure Subscription ID
    • AAD_TENANT_ID to be your AAD Tenant ID
    • AAD_SERVICE_PRINCIPAL_ID to be the ID of the service principal you created
    • projectName to be 3-10 different letters. Perhaps letters in your name or your initials.

    Some of the resources need to have globally unique names and most of the names are created using projectName, projectLocation, and suffix (from vars-dev and vars-prod). Changing any of those should do the trick, but we are at the mercy of what resources others have already created to avoid naming conflicts.

    Unfortunately the names can't get too long though either, as storage accounts can only be 24 characters long.

  3. Throughout this project many of our pipelines are set to run dev only off a certain branch, and prod off a different certain branch. For us those branches were main and release. If you want those branches to be different then do a find and replace of refs/heads/<name> to be whatever you want it to be.

    If your Azure DevOps default branch is master, then you will either need to change the conditions or rename your default branch to main.

  4. Create an Azure Resource Manager service connection from your Azure Dev Ops instance to whatever Azure subscription you want to deploy your resources to. This service connection should be at the subscription level, not to a specific resource group. Make sure "Grant access permission to all pipelines" is checked.

    You might want to create one dev service connection and one production one. Remember the names of both.

  5. Update the pipeline files to use your newly created service connections.

    There are 3 different pipelines that use these service connections that you will need to update. For all of them update <environment>ServiceConnection variable in the variables: section of the file. The value you will set it to is whatever you named your service connection.

    Be sure to update both the dev and prod service connection values for all of them.

  6. Now that you have service connections created you will need to find something called the objectId for those and update other variables.

    You will need to update the servicePrincipalObjectId value in both vars-dev.yml and vars-prod.yml. The devops-pipelines.md has a section titled Getting servicePrincipalObjectId that will walk you through how to get that.

    Note: This is for the Azure Dev Ops Resource Manager service connection created through Azure Dev Ops not the one created through cli at the very beginning

    If you created a different dev and prod service connection the two values will be different.

  7. Now you will need to create Azure Pipelines using the existing yaml files provided in this repo. If this is your first time creating a pipeline this link is slightly helpful Create Azure Pipeline from existing YAML

    There are 4 pipelines you need to create, and another 4 we recommend. We recommend naming all of them what we have, but we will call out any changes that need to be made if you name them differently

    For in depth documentation on all the pipelines, consult the devops-pipelines.md document

    Pipelines you need to create:

    1. CD - IaC

      This is the pipeline that will deploy all our Azure resources. It is from the cd/iac.yml file

    2. CD - IoT Edge Modules

      This is the pipeline that will deploy all the edge modules to iotedge. It is from the cd/iot-edge-modules.yml file

    3. CI - IoT Edge Modules

      This is the pipeline that builds and pushes the artifacts we will deploy to iotedge. It is from the ci/iot-edge-modules.yml file.

      Important

      If you choose to name this pipeline something other than CI - IoT Edge Modules then you will need to update the resources->pipelines->pipeline->source value in the cd/iot-edge-modules.yml file file. That source must match the exact spelling of your pipeline name for deployments to automatically trigger.

      Leave the resources->pipelines->pipeline value the same, only change the source value if you opt to name your Azure Pipeline something other than CI - IoT Edge Modules.

    4. CI - LVA Console App

      This is the pipeline that builds and pushes the artifacts used to start LVA and trigger object detection and recordings once everything is deployed. It is from the ci/lva-console-app.yml file.

      Important

      If you choose to name this pipeline something other than CI - LVA Console App then you will need to update the resources->pipelines->pipeline->source value in the cd/iot-edge-modules.yml file file. That source must match the exact spelling of your pipeline name for deployments to automatically trigger.

      Leave the resources->pipelines->pipeline value the same, only change the source value if you opt to name your Azure Pipeline something other than CI - LVA Console App.

    Pipelines we recommend creating:

    1. CI - Docs

      This is a pipeline that runs CI if documents are updated. It is from the ci/docs.yml file

    2. PR - Docs

      This is a pipeline that does PR validation if you are updating documentation. It is from the pr/docs.yml file.

    3. PR - IoT Edge Modules

      This is a pipeline that does PR validation for changes to any iotedge modules. It is from the pr/iot-edge-modules.yml file.

    4. PR - LVA Console App

      This is a pipeline that does PR validation for changes to LVA Console App. It is from the pr/lva-console-app.yml file.

    All of the PR pipelines (if created) will not automatically trigger unless you add it as a build validation for a certain branch. We recommend adding all of them as build validation policies to protect your main branch.

    This link explains how to do that: How to add Build Validation Policies

    We recommend doing proper path filtering so they are only triggered for relevant file changes.

Deployment/Resources Setup

Now that all the pipelines are created and the values are updated we can start deploying things!

The remaining steps left to take for deployment are:

  1. Trigger the CD - IaC pipeline to run off the main branch. When this pipeline runs all of your Azure Resources will get created in the subscription you created the service connection for. They will all be deployed to the resource group according to how you named it in vars-common.yml

  2. Now that the keyvault has been deployed you will need to add one more secret to it manually.

    In order to do this you will need to add yourself to the keyvault access policies.

    Now that you have permissions to view and create secrets, create a secret named aadSpSecret with the value of the service principal secret you created at the very first step. Told you you'd need to use it again :-).

  3. Next, there are a couple of secrets from keyvault that are needed by CI and CD pipelines. To provide access to this we will be creating two variable groups, secrets_dev and secrets_prod, in Azure Pipelines. Both of these are needed to run the pipeline, so if you haven't deployed to a prod environment just create a placeholder secrets_prod. Another option is to delete the prod sections of the pipelines, but only do this if you know you won't want a prod environment in the future.

    These variable groups are created under Pipelines->Library and each of them will be linked to keyvault. Follow these steps to link your secrets from keyvault.

    The specific secrets we will need to access from our pipelines are:

    • aadSpSecret
    • acrPassword
    • iotHubConnectionString

  4. Now that the secret groups are created we can run our two necessary CI pipelines. Trigger CI - LVA Console App first and then CI - IoT Edge Modules to run off the main branch.

    Once these have both completed the CD - IoT Edge Modules will be automatically triggered and your modules will be deployed and object detection on the edge will have started!!!

Validating Deployments

To see the objects that are detected you can look at the media service resource assets in the Azure Portal. This is where video recordings will be uploaded. You can also connect to the VM using Bastion and look at the objectDetectionBusinessLogic logs (command to run is iotedge logs objectDetectionBusinessLogic). This will show you what objects are getting detected in your video.

To stop the object detection you can shut down the VM or follow our environment setup instructions and the documentation in LVA Console App and run the console app with the operations_teardown.json file :-). This is the option we prefer of course :-), but both would work.

One last note:

We recommend adding a manual approval gate for prod deployments, so that the prod environment can only be pushed to after someone with authority approves. This link will explain how to do that.

Development

If you want to develop your own business logic for edge object detection, these sections will provide more information on how that can be accomplished!

Documentation

Projects

  • Edge - This project contains all Edge modules and deployment manifests
  • LVA Console App - This project contains the console app that will run direct method operations on the LVA module running on the Edge device

Prerequisites

Environment Setup

Linting

See the lint section in the environment setup document