Right now we're setting up survey line for the first line of of mines.
We do four lines of 25 because we're lucky enough to have more than 100 inert mines.
These two just came from Ukraine.
It's not easy to find places where you can lay dormant mines and fly drones.
We're trying to automate detection of different types of landmines, anti-personnel
mines, anti-tank mines, and we're doing this through remote sensing and drones.
Currently, most of humanitarian demining is manual, so this is a beep and prod method
where we have metal detectors, lay out grids, and you're able to scan a metal
detector really low down to the ground and poke it to see if there is a mine there.
It can be quite dangerous and we want to really increase the efficiency of this task.
It can take a very long time to clear a field that has absolutely no mines in it.
So we want to be able to find a field that actually does have mines and make that a high
priority of clearance.
Drones are relatively safe.
We don't have to step in the field to assess it.
So we're flying over, taking surveys of mine fields, and then we're using machine learning
to help us automate the detection afterwards.
Okay, but hold on. Hold on.
That's right. Often people conflate the terms machine learning and artificial
intelligence. Artificial intelligence includes every application where a machine is
designed to imitate human perception.
Machine learning is more specific.
It is when a machine learns just from the data that you give it the features of the
object that you wish to detect.
So humans are not very good at deciding what makes a chair, a chair or what makes a
landmine a landmine.
Machines are.
So the neural network detects landmines just from the data that you give it.
So we give it varied data and lots and lots of labeled data.
That's why we're out here to collect lots of real world data.
Machine learning always works best with real world data.
We have an algorithm that's able to detect the PFM-1 plastic landmine that's actually
now being used and deployed in Ukraine.
Where we want to go with this project is be able to detect many types of cluster
munitions and land mines.
So the total goal would be to fly over with different sensors, either a thermal sensor,
multispectral or visual input the imagery into the algorithm, and it'll output the
coordinates of where different mines lay.
And we'll also say what type of mine they are.
It's sort of blends in. But so this is the PFM-1.
These go off from cumulative pressure and they go off from approximately 15 kilograms
of cumulative pressure.
So it's designed that way for a pretty nasty purpose.
They don't blow up immediately when they're dropped or when animals walk on them.
They're designed to explode upon handling.
They're mostly children, they're mostly civilians, and they're designed to maim.
Fire in the hole.
We did two blasts yesterday.
You can see the holes are a little bit different.
And the reason why we want to look at these holes is to try to understand what we can
detect, land mines, but can we also see presence of mine so we can see things like
crater holes and it's a lot of indirect evidence we can use.
This is basically the field as it will as it will look.
We label all the mines that we lay out, as well as the ground control points.
These ground control points are used for creating a centimeter scale, accurate ortho
mosaic. That's just a big picture of all the pictures that we're going to take with the
drone stitched together.
Convolutional neural networks are scarily accurate and a lot of applications that are
better than humans. We've achieved a 92% accuracy with the limited amount of data
we've gotten in this field season, we're collecting a lot more data, and with machine
learning, it's always better to get the data that you want initially and not have to
create the data that you want with post-processing afterwards.
We're using visual sensors, so a lot of mines are on the surface that are scatter-able, but
oftentimes mines are obscured by tall grasses.
They can be obscured by some kind of sand.
So that's really where thermal and multispectral imaging comes into play and can
help us detect mines that humans can't see.
We'll label the imagery, we'll drop boxes around all the mines and label them with
their classification.
After that, we're going to split them again into manageable sizes for the convolutional
neural network and feed them in as training data.
We're going to train the network and then test it and evaluate it.
It's windy. It's very windy.
It's going to be fine for most of the drones, this one is a little bit older, It
needs special care.
Yeah, in Oklahoma, you kind of never know.
It's never been this windy, but it's also never been this cool.
So it's usually like 99.
So there's pros and cons.
This is just a DJI Phantom four.
So these are off the shelf.
We try not to retrofit as much as possible.
We want to make this method really easy and user friendly.
We don't want it to be some specialized drone that costs a lot of money and only we
produce. We want this to be like universally easy to find and use.
You can see the mines like if you look close, they take some time to thermally
equilibrate with the environment with a thermal we try to wait about an hour and we
did today. Something that I wish
people knew more about is actually landmines as a problem globally.
7000 people have been injured or died this last year.
Some estimates of active landmines.
It's anywhere from 110 million to a little bit less than that.
It's hard to quantify, but in the millions of numbers, they're found in over 60
different countries and regions throughout the world.
So they're very widespread and mostly in post-conflict nations.
Mines can last decades after a war is over.
In the Soviet-Afghan war, which took place in 1979 and onward, there's still mines today
that people are stepping on and being killed from.
The plan with our project is to have a self contained offline method to help de-miners.
It is to help certain teams detect the mines that they're looking for more efficiently,
more effectively and safer.
Me and Gabriel have co-founded the Demining Research Community.
It's a non-profit organization that focuses on landmine detection technologies and
development. We want to bridge academic research with humanitarian demining, and
we're trying to apply qualitative scientific method for detecting landmines and in
communication with mine action organizations to see what they actually need in the field.
The end goal is to give a non-profit de-mining organization a big pelican case
with a computer that's designed just to process this data.
And drones and sensors that are designed just to take pictures of mines.
The next step is for us to test this on real minefields in Cambodia, and we're hopefully
going to go out there and be able to fly and test our method in quite challenging
situations to detect mines in the real world.
If it scales well there, then I think we'll be ready.
This is GCP one.
So draw a white square here and write "white."
I think the main thing that I've learned from coming out into the field for these two weeks
is just how hard I can work and just how comfortable I can be with being
uncomfortable. And the only thing that enables me to do that is that I believe that
we can save lives.
And I believe that if I save lives, I can die happy.
Is that too dramatic?