>> Building into any solution might not be trivial.
If you're a visionary expert,
delivering a turnkey solution to a customer might be a bit tricky.
Azure IoT is central to the rescue,
as well as IoT Edge in that case.
Yusuf from DDI lab is here to share his experience,
leveraging IoT Central,
IoT Edge to implement vision of AI solutions for their customers,
that's today on the IoT Show.
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>> Hi everyone. You're watching the IoT show.
Thanks for tuning in my name is Olivier,
I'll be your host today.
Today we'll talk about a very interesting solution,
putting together Vision AI and IoT Central to
deliver new ways of dealing with workflows.
For that, we have Yusuf, Yusuf, how are you?
>> Hi, Oliver, I'm good thanks, yourself?
>> I'm great and fantastic.
I'm not super healthy today,
but that's fine, we'll make it work, how about you?
>> Yeah, I'm doing well quite
early this morning, but everything's good.
>> Where are you calling from?
>> I'm actually based in Sri Lanka,
but our company is an Australian company located in Sydney.
>> Well, what is that company
and what is it that you guys are doing?
>> DDI Labs, we are an AI startup and
we use computer vision to drive data decisions.
We work with various different government agencies and
private companies to unlock the full potential of video data.
>> Nice. Here you came to show us one
of your solutions that really highlights in
a Vision AI and how it can enhance
this workflows in people's life and enhancing the workflows,
but like simplifying operators life and so on.
We'll see that in a second and you're
leveraging anxiety central and
we'll look at that as well so tell
me about the solution you're going to show today.
>> Exactly. There is a little bit of background on the customer,
International Tower Sydney is
a large commercial precinct managed by JLL and they've got
three loading docks which manage around
350-400 deliveries per day as well as casual vehicles.
The problem they had was the site was managed
by three full time guards for each section of
the dock and then there was a dock mast on top of
that had to like validate every single booking.
Tasks that included updating booking statuses,
manually checking the vehicle height,
opening the boom gate,
making sure there are available car spaces.
There was quite a few basically mundane tasks
that the guards had to do.
We at DDI found an opportunity
here to reuse the existing CCTV cameras on site
to automate these tasks and
now the system we built now
enables full automation of valid bookings.
Automated boom get access on
whitelisted makers a custom
dashboard that allows the dock mask to see
the vehicle height as it enters
all the available base and
which vehicle is parked in a given bay in just a matter of clicks.
>> Nice and your solution
we discussed that when we were preparing,
it's not about replacing humans,
it's really about empowering them to be more efficient,
to have less prone errors happening.
It's really not about replacing
human it's really about amplifying,
augmenting the work they can do, right?
>> Exactly yes. The key success was that it
now allows the dock master and the guards
to do more complex tasks and they efficiently use their time.
>> Yeah, make their work more interesting.
>> Yeah, exactly.
>> Awesome. Show me a bit of the solution,
we'll talk about the details and what's under the hoods after.
>> Excellent. As you can see on the screen here,
we've got LPR camera on entry and this captures
the license plate recognition off
the vehicle and this drives the automation process.
We've also developed a custom flutter application
that sits on a tablet on this portico to stand here,
and it will be used to scan
the booking of the driver and validate with
the existing booking system on site, sorry.
>> Go ahead. I'm just nodding, I like it.
>> As the vehicle drives through,
the driver would scan the QR code
and while it's validating the bookings,
our PR camera will also capture the license plate,
you should be able to see that here.
>> That's a real that's time feed right now,
you didn't even plan that.
>> Exactly, the vehicle entered.
Then once the vehicle enters on site,
our Vision AI kicks in,
you can see the driver coming in,
scanning the booking,
it's validating the booking or he's having a chat.
>> He was having a chat [inaudible].
>> As the booking got accepted,
the boom get open and then the vehicle entered.
>> Got it.
>> This is our Vision AI as you can see,
it's running on all the cameras on
the dock that's looking at the parking bay.
If we use a YOLO based inference engine,
we've done quite a bit of local training
as well to improve that accuracy.
Because we know the camera ID and we can geo map the bay,
we're able to identify which bay ID is looking at what camera.
As the vehicle moves from
one camera to the next and parks at a given bay,
we know exactly which bay that is.
>> You were saying you're reusing
the existing CCTV infrastructure
so you don't even have to deploy a new set of cameras, right?
>> Exactly. That's one of the key features of our solution,
is that we try to minimize the cost for
our customer and whenever possible,
we try to use existing infrastructure.
>> The inferencing happens on site as well,
you have a box,
is it only one box that does
inferencing for all these cameras at once?
>> We've got one dedicated server on site,
and that basically runs this full solution,
it's dedicated for our solution.
>> Awesome. It's pretty,
lightweight I would say,
in terms of of installation.
>> Definitely.
>> Okay.
>> As the vehicle comes in, basically,
I'll check with the sign into checking ID and this
will allow us to track it from one camera frame to the next.
As it moves on from a camera frame,
we can pass the tracking ID onwards and that's how
we're able to track the vehicle across the whole loading dock.
>> Okay.
>> Then finally, a custom dashboard as you can see,
this is a real time view of the dock master sees.
They can easily identify what bays are available and which are
occupied as this is
the same layout of the dock they have so visually,
it's easy to understand for them.
>> Previously, they were using
a physical map, and they were just, like,
keeping tabs on who's where or
just a table on a paper or something like that, right?
>> They had guards on site that would walk around and basically
check the vehicles that were
parked because it's quite a large dock.
Quite often people don't actually park on the bay they're given,
it's hard to check as well.
>> Got it. You just hover and
you have the the license plate that shows up, right?.
>> The dock mask can easily just click on a bay,
see the plate if it's got a booking attached to it,
they've got the booking ID here as well,
so they can easily check.
If it's a purple, it means the vehicle has
overstayed its booking so they
can easily take action based on the color codes of the tiles.
>> Got it.
>> On top here, we have showing like
an overview, they know, okay,
dock one is pretty much almost full,
when the next vehicle comes,
we'll send them to dock two and so forth.
>> I love it.
>> We've added a couple of other features
as well on this dashboard,
one is the ability to wait list and blacklist vehicles.
For example, if a white listed vehicle does come in,
the boom gate would just open straight away,
allowing the vehicle in and if a blacklisted vehicle comes in,
an alert will pop up and then the dock master
can take the relevant actions after that.
>> Okay.
>> Yeah.
>> I would say that from here on,
the interesting thing is that
scenarios are limitless because you have
all the data and then
the scenario basically is usually adapted to your customers,
so you will adapt your solution to
the customer needs and scenarios here, right?
>> Exactly, that's the beauty of it because
we developed step by step.
We've added a lot more features
now than we currently did at the beginning.
>> Okay.
>> Yeah, it's really easy because we
install them in certain microservice modules.
As we develop new features,
we just have to add on another module
and remove the module or stuff like that.
>> Got it. The title of the video mentions Azure IoT Central.
Ell us, how are you using because
here we're not seeing IoT Central for now.
How are you as DDI Labs
leveraging and using IoT Central in this case?
>> That's an excellent question. As you can see,
a solution like this has a lot of different software,
hardware components that need to get integrated together.
We are currently using IoT Central
now as a way of managing this whole solution.
Internally for us as DDI Labs to manage, monitor the solution,
make sure it's running smoothly,
that is our core feature of IoT Central,
I'll just share with you the IoT Central dashboard.
>> Okay, let's look at that.
>> As you can see, this is our central dashboard.
We've got various different device template versionings,
and this is actually a real good benefit of using IoT Central.
As I told you initially,
we did not have the full solution.
We added features in step by step.
This allowed us to create a new version
and update it to the next version.
Test it. If things didn't work out,
we could easily migrate it back down to the previous version.
We had minimal downtime in the system.
This allowed us to continue to develop and then make changes.
This is actually the device.
>> You're leveraging all the features
of IoT Central in terms of managing
the IT edge devices because
IT edge is what's powering the server,
you were mentioning earlier on.
>> Exactly. Our server's running IT edge and
that's sending the data upstream IoT Central.
>> You can from here, from that dashboard, you can say, hey,
I want to deploy a new model for
recognizing new type of vehicle or optimizing my models.
Because they' re yellow ones, but you can
definitely retrain them at will,
and then you can from here,
on IoT Central,
with your, let's say, administrator view,
you can now deploy the new version of
a model onto these boxes that are out there.
>> Exactly. When there's a new version
released or a different model that we want to use,
we can easily upload it from IoT Central.
We just put in the path,
install it on the server, and then run it from there.
>> Got it. Are you using
the multi-tenancy features or you could eventually,
because you have different customers with similar solution.
You can eventually manage their boxes in a same interface
using multi-tenancy supports in
the form of organizations in IoT Central.
>> Exactly. At this point,
we are not actually using that.
We've been creating a new app with a new resource group for
each customer just for privacy
and data at the resources are not shared.
But that's something we definitely have
been using in smaller projects.
Not these larger projects.
>> Got it. Interesting.
>> You wanted to show me a bit more about the devices.
>> No. Go ahead. That's fine.
>> I wanted to ask you about the extensibility.
We saw a custom map and UI that the dock master will be using,
that is not an IoT Central UI.
There's something that is delivered
for the customer in the form of a web page.
They have all log in for their personnel, I guess.
Did you leverage some of the extensibility features
of IoT Central for that?
>> Exactly. Our whole project initially
started with the end dashboard being on IoT Central.
As you can see, we've created custom dashboard using IoT Central.
This is the overview tab and then this is the map.
As you can see, visually,
this is hard for the dock master to identify,
hey, where is Bay 1 in terms of the loading dock?
This is where we reach a limitation for IoT Central.
What we are doing now is, we are
basically exporting the data from
IoT Central itself to our custom dashboard.
All the data is coming from IT edge device to
IoT Central and then getting pushed out to our custom dashboard.
>> Got it. I don't know if it's
an easy thing to estimate right now like that,
but would you say that IoT Central
saved you a lot of developing time?
Because at the end of the day,
it's about managing the connectivity securely to the devices,
gathering the data, managing
the runtimes for IoT Edge as well as the models running on them.
You're doing the maintenance for
the customers off the IoT Edge runtime end the models.
All of that from IoT Central,
I would guess saved you a lot of time.
>> A lot. It's so easy now because basically from this portal,
we can monitor each module.
As you can see, we have various different modules
here for different parts of the solution.
We can view the logs,
we can restart the modules,
we can run commands.
We've also exporting the system connectivity events.
If there is an issue,
actually last week we had an issue where
the Internet had gone off and the system was offline,
we got an alert straightaway saying, hey,
something is wrong, investigate this. Then we took action.
>> Nice.
>> Yes. IoT Central has made things a lot easier for us.
Also the fact of replicating this exact solution in another site
or similar solution in another site
using IoT Central it's only a few steps away.
We just have to create a new device,
make sure the cameras obviously are configured and whatnot.
But on the software side of it,
it won't take long to replicate
the solution and also develop other solutions.
We've built different solutions using similar modules.
Because the modules are already there,
it's not hard for us to just pick
a few modules and build another solution completely.
>> Yeah. What other solutions?
Because when you look at
your website DDL Labs is really about visionary AI.
That's your domain of expertise.
IoT Central takes care of all the device management and so on,
and then you export the data to customize a solution.
Your realm is visioning AI.
What scenarios you guys are
leveraging visionary AI for these days?
>> Another big project important in Australia is
chain of responsibility. It's a course check.
What we do is we basically ensure any vehicle coming on site
is fully registered and meets all the compliances for that site.
If not, we do raise alerts to the guard at the entrance to, hey,
I do not allow this vehicle on-site or to verify that they've
actually purchased a new registration
and it's just not updated in the system yet.
That's another big project and smaller ones
like people counting at licensed venues.
Because once COVID hit, actually,
there was a lot of capacity restrictions
and number of people in a certain area.
What we did was, we basically
installed a paper counting system on
the existing cameras on site and we were
able to count occupancy for the customers.
>> Brilliant. I like the way you're making real all
these fake demos we
used to have when we started talking about vision AI.
They are real scenarios and you guys are
delivering that to your customers. It's brilliant. I love it.
>> Thanks. Thank you.
>> Awesome. Yusuf thanks a lot for joining the show today.
If people want to learn more about DDI labs and your solutions,
they can go to this link below.
aka.ms/iotshow/DDILab. Thanks for your time, Yusuf, once again.
>> Thanks a lot, Olivier. Have a good one.
>> Have a good everyone. Bye.
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