At any given time around the world, an active wildfire is causing tremendous
devastation. And it is not just forest fires that cause immeasurable damage.
Structure fires in urban areas also pose a significant problem. In the
Challenge 1 “Fight Fires with Flyers” we asked you to come up
with creative ways firefighters could use drones to protect and save lives.
It was the first in a series of interactive coding competitions using a
robotic drone kit
from NXP. This allowed talented developers across the globe to address some of
the biggest challenges facing society – and wow, did you all respond!
Your ideas showed that fighting fires is more than just dowsing flames. From
racking movements of flame pools, watching the front line of fire where sparks
fly to identifying people that are lost on trails – the judging panel
has been impressed by your innovative and unique ideas to assist firefighters
in their disaster response. But see for yourself in our highlight video:
We realise how cliché this sounds, but the number of high-quality entries
made it genuinely difficult to select the final winners. So firstly, a huge
thank you to everyone who entered! It’s clear how much time and energy
has gone into each submission, and seeing how you have blended different
technologies together to solve real-world challenges has been truly inspiring.
But without further ado, we’re delighted to reveal which entries
achieved the coveted podium positions among the participants. To qualify for
the top three winners, participants were asked to submit a storyline
documenting how their idea helped first responders, as well as a detailed
step-by-step description of how they implemented their project. They also had
to document their unique approach with pictures and videos. Finally, they were
asked to submit a code so that their ideas could inspire others.
The Winning Entries
Drones, among their many other features, are good at scanning and quickly
covering a large area. This is particularly valuable for disaster relief in
case of fire. By adding an additional sensor to the drone robot, it can be
used to locate people in need. Our
first place winner
does just that.
First place: Dobrea Dan Marius’s Autonomous Human Detector Drone
First place: Dobrea Dan Marius’s autonomous human detector drone
One of the main objectives in firefighting is to prevent loss of life. Further
it is critical to quickly identify and assist people who have succumbed to the
fumes or are lost in debris. The objective of this project is to develop a
real-time drone technology system capable of detecting humans in catastrophic
conditions such as fires.
In addition to autonomously flying, Dobrea Dan Marius’ drone derives
position data from NXP’s
which feeds information to the attached subsystems. With this position
information available as a reference, onboard cameras feed video material
through a machine learning model that recognizes the human with high accuracy.
The drone further has a sonar object-detection system, which is essential to
enable it to work autonomously.
This project shows that even a limited processor can be of great benefit for
Dobrea Dan Marius has done a great job at documenting his work and code,
including videos, images and schematics. He explains challenges he faced along
the way, how he overcame them, and areas that require attention to put this to
real use. We are pleased to hear that the challenge was also beneficial for
himself: “The HoverGames Drone is an amazing device, from where I learned a lot and
I had a great time building the hardware and developing the software
Congratulations, Dobrea Dan Marius! You’re the winner of the first
HoverGames Challenge on Fight Fires with Flyers.
Second place: AK’s machine learning fire-class analyzer
Second place: AK’s Machine Learning Fire-class Analyser
Different types of fire need different responses.
helps firefighters identify the cause of a fire, then recommends the most
appropriate means of putting it out and the size of crew required. His drone
includes a range of fume sensors that feed data into a machine learning model,
which in turn predicts the type of material that’s burning.
What particularly impressed us was the way AK trained the machine learning
system, rather than using a pre-trained model, and continually refined it to
drive up the accuracy of its predictions.
Third place: Tatsuya Iwai’s Lightning-detection System
Third place: Tatsuya Iwai’s lightning-detection system
Tatsuya points out that lighting is a significant cause of wildfires –
and it can happen pretty much anytime, anywhere, including in very remote
helps authorities keep their fingers on the pulse by sensing lightning at
distances of up to 40 km. He also tested a means of detecting burning fires
with the drone, including using image recognition and thermal sensors.
We liked the way he used images of fire to test his drone safely as well as
his excellent documentation on GitHub.
We are amazed about the number of high quality submissions for the
HoverGames Challenge 1. After seeing your innovative ideas and enthusiasm, we are rewarding every
submission* with a newly designed
i.MX 8Mmini companion computer
for vision processing and mobile robotics. The same processor will be used in
the next challenge. How cool is that!
HoverGames Challenge 2: Starting Spring 2020
Are you inspired by the way participants have used drones to tackle disasters?
for details on the second challenge starting in 2020.
At NXP, innovation is always now, but our focus is always the
future. Our dedicated team of experts is united by a passion to
make everyday life more remarkable through technologies that
continually redefine life as we know it.