Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify the garbage known as floating marine macro-litter, or FMML, within images of the sea surface.
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References
- Odei Garcia-Garin et al., Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R, Environmental Pollution, https://doi.org/10.1016/j.envpol.2021....
- Code & web app: https://github.com/amonleong/MARLIT
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Chapters:
0:00 - Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.
0:30 - Floating marine macro-litter
2:19 - The method
5:10 - Conclusion
0:30 - Floating marine macro-litter
2:19 - The method
5:10 - Conclusion
Video Transcript
00:00
an ai software able to detect and count
00:03
plastic waste in the ocean
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using ariel images it's both clever and
00:07
simple
00:08
and you could use this same model for
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many image classification applications
00:12
let's see how it works
00:16
[Music]
00:21
this is what's ai and i share artificial
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00:31
we live on a blue planet over 70
00:35
of earth is covered by sea from space
00:38
our ocean appears pristine clean
00:42
unfortunately it's not because of poorly
00:45
controlled waste sites
00:46
illegal dumping and mishandled waste
00:49
from population centres
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tourism industrial and agricultural
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activities
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an estimated 8 million metric tons of
00:57
plastic
00:58
waste entered the oceans
01:02
aude garcia garion et al from the
01:04
university of barcelona
01:06
have developed a deep learning based
01:08
algorithm able to detect and quantify
01:10
floating garbage from aerial images
01:13
they also made a web-oriented
01:15
application allowing users to identify
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these garbages
01:18
called floating marine microliter or fml
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within
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images of the sea surface floating
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marine macro litter is any persistent
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manufactured or processed solid material
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lost or abandoned in a marine
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compartment as you most certainly know
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these plastic wastes are dangerous for
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fish turtles and marine mammals as they
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can either
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ingest them or get entangled and hurt
01:42
traditional approaches to detecting
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these
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fmls are observer-based methods
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meaning that they require someone on a
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vessel or airplane to look for them
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yielding to precise identification but
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extremely expensive and time demanding
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labor
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fortunately this detection can be done
02:00
using cameras or sensors on aerial
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vehicles
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but it also requires trained scientists
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to manually look at the collected data
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being again extremely time consuming
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automation is clearly needed here and
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could help us
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improve the quality of our marine
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compartments worldwide
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much more effectively this is where
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machine learning
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and deep learning commonly deep learning
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proves over and over
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that it's a very powerful automation
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tool and especially in the computer
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vision industry
02:29
where it's known to automatically
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identify the important features of an
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image
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without any human supervision making
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this approach
02:37
less time demanding than its
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predecessors for many different
02:40
applications including
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this very important one as you may
02:44
suspect
02:45
they use the convolutional neural
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networks to attack this problem
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this type of neural network is the most
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commonly used deep learning architecture
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in computer vision
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the idea behind this deep neural network
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architecture is to mimic the human's
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visual system if you want to learn more
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about the foundation of convolutional
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neural networks
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or cnns i will refer you to their video
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on the top right corner on your screen
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where i'm explaining them more in depth
03:11
they train their algorithm with real
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images like this one
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taken by drones and aircraft with
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annotations made by the same
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professionals that are usually
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manually analyzing them this is a
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challenging task even for deep learning
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because of all the possible variations
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in colors and sun reflections as you can
03:29
see here
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in short their model is a regular binary
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classifier
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cnn architecture composed of
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convolutions and poolings
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terms that i explained in the video i
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referenced earlier
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that outputs a binary response telling
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us if there are fmls or not in the
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picture
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the depth of the network is due to these
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convolution layers
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compressing the image and creating many
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feature maps
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which are the outputs of the filters
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ending with a general representation
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of the image allowing us to know in
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general
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what the image contains such as fml in
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this case
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note that this exact same architecture
04:06
could have been used on
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any other computer vision application
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with a test to classify whether or not
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something is in the image such as
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putting a defect on a manufacturer part
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or telling if there is a dog or not what
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they did differently making it powerful
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to fml detection
04:22
is that they had the idea to split the
04:24
image into 25 smaller cells
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that each outputs a classification
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result fml
04:30
or not yielding much better overall
04:32
accuracy
04:33
then they used the shiny package of r
04:37
to develop their application their
04:39
algorithm allows the detection and
04:41
quantification
04:42
of fmls as well as providing support to
04:45
the monitoring and assessment of this
04:47
environmental threat
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however it's still not completely
04:50
automated yet and requires a human in
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the loop
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as of now they are still looking for
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more annotated data to allow their
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algorithm to also
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identify the size color and type of fml
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which are very relevant information for
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planning well-targeted policy and
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mitigation measures
05:07
this is still an amazing application of
05:09
deep learning with a great use case that
05:11
will benefit everyone
05:13
of course this was just an introduction
05:15
to this new paper
05:16
and i linked both the paper their code
05:18
and their application
05:20
in the description below if you would
05:21
like to read more about it or even
05:23
try it out yourself please leave a like
05:26
if you went this far in the video
05:28
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05:32
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05:36
thank you for watching
05:40
[Music]