Using AI to Detect and Count Plastic Waste in the Ocean

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

Video Transcript

00:00

an ai software able to detect and count

00:03

plastic waste in the ocean

00:04

using ariel images it's both clever and

00:07

simple

00:08

and you could use this same model for

00:10

many image classification applications

00:12

let's see how it works

00:16

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00:21

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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

00:51

tourism industrial and agricultural

00:53

activities

00:54

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

01:17

these garbages

01:18

called floating marine microliter or fml

01:21

within

01:22

images of the sea surface floating

01:25

marine macro litter is any persistent

01:27

manufactured or processed solid material

01:30

lost or abandoned in a marine

01:32

compartment as you most certainly know

01:34

these plastic wastes are dangerous for

01:36

fish turtles and marine mammals as they

01:39

can either

01:40

ingest them or get entangled and hurt

01:42

traditional approaches to detecting

01:44

these

01:45

fmls are observer-based methods

01:48

meaning that they require someone on a

01:50

vessel or airplane to look for them

01:52

yielding to precise identification but

01:55

extremely expensive and time demanding

01:57

labor

01:58

fortunately this detection can be done

02:00

using cameras or sensors on aerial

02:02

vehicles

02:03

but it also requires trained scientists

02:05

to manually look at the collected data

02:08

being again extremely time consuming

02:10

automation is clearly needed here and

02:12

could help us

02:13

improve the quality of our marine

02:15

compartments worldwide

02:16

much more effectively this is where

02:19

machine learning

02:20

and deep learning commonly deep learning

02:23

proves over and over

02:24

that it's a very powerful automation

02:26

tool and especially in the computer

02:28

vision industry

02:29

where it's known to automatically

02:31

identify the important features of an

02:33

image

02:33

without any human supervision making

02:36

this approach

02:37

less time demanding than its

02:38

predecessors for many different

02:40

applications including

02:41

this very important one as you may

02:44

suspect

02:45

they use the convolutional neural

02:46

networks to attack this problem

02:48

this type of neural network is the most

02:50

commonly used deep learning architecture

02:52

in computer vision

02:54

the idea behind this deep neural network

02:56

architecture is to mimic the human's

02:58

visual system if you want to learn more

03:00

about the foundation of convolutional

03:02

neural networks

03:03

or cnns i will refer you to their video

03:05

on the top right corner on your screen

03:07

where i'm explaining them more in depth

03:11

they train their algorithm with real

03:13

images like this one

03:15

taken by drones and aircraft with

03:17

annotations made by the same

03:19

professionals that are usually

03:20

manually analyzing them this is a

03:23

challenging task even for deep learning

03:25

because of all the possible variations

03:27

in colors and sun reflections as you can

03:29

see here

03:31

in short their model is a regular binary

03:33

classifier

03:34

cnn architecture composed of

03:36

convolutions and poolings

03:38

terms that i explained in the video i

03:40

referenced earlier

03:41

that outputs a binary response telling

03:43

us if there are fmls or not in the

03:46

picture

03:46

the depth of the network is due to these

03:48

convolution layers

03:50

compressing the image and creating many

03:52

feature maps

03:53

which are the outputs of the filters

03:55

ending with a general representation

03:57

of the image allowing us to know in

03:59

general

04:00

what the image contains such as fml in

04:03

this case

04:04

note that this exact same architecture

04:06

could have been used on

04:08

any other computer vision application

04:10

with a test to classify whether or not

04:12

something is in the image such as

04:14

putting a defect on a manufacturer part

04:16

or telling if there is a dog or not what

04:18

they did differently making it powerful

04:20

to fml detection

04:22

is that they had the idea to split the

04:24

image into 25 smaller cells

04:26

that each outputs a classification

04:28

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

04:48

however it's still not completely

04:50

automated yet and requires a human in

04:52

the loop

04:53

as of now they are still looking for

04:55

more annotated data to allow their

04:57

algorithm to also

04:58

identify the size color and type of fml

05:01

which are very relevant information for

05:03

planning well-targeted policy and

05:05

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

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05:28

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05:36

thank you for watching

05:40

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