TY - JOUR
T1 - An intelligent way for discerning plastics at the shorelines and the seas
AU - Kylili, Kyriaki
AU - Hadjistassou, Constantinos
AU - Artusi, Alessandro
N1 - Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.
AB - Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.
KW - Artificial intelligence
KW - Environmental monitoring
KW - Image classification
KW - Machine learning
KW - Marine pollution
KW - Object detection
KW - Plastics
UR - http://www.scopus.com/inward/record.url?scp=85088553759&partnerID=8YFLogxK
U2 - 10.1007/s11356-020-10105-7
DO - 10.1007/s11356-020-10105-7
M3 - Article
C2 - 32712938
AN - SCOPUS:85088553759
SN - 0944-1344
VL - 27
SP - 42631
EP - 42643
JO - Environmental Science and Pollution Research
JF - Environmental Science and Pollution Research
IS - 34
ER -