Object Detection

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Related papers


K. Kylili, A. Artusi and C. Hadjistassou

A new paradigm for estimating the prevalence of plastic litter in the marine environment

The intelligent method proposed herein is formulated on a deep learning technique which can identify, localise and map the shape of plastic debris in the marine environment. Utilising images depicting plastic litter from six beaches in Cyprus, the developed tool pointed to a plastic litter density of 0.035 items/m2. Extrapolated to the entire shorelines of the island, the intelligent approach estimated about 66,000 plastic articles weighting a total of ≈1000 kg. Besides deducing the plastic litter density, the dimensions of all documented plastic litter were determined with the aid of the OpenCV Contours image processing tool. Results revealed that the dominant object length ranged between 10 and 30 cm which is in agreement with the length of common plastic litter often spoiling these coastlines. Concluding, only in-situ visual scan sample surveys and no manual collection means were used to predict the density and the dimensions of the plastic litter.


title={A new paradigm for estimating the prevalence of plastic litter in the marine environment},
author={Kylili, Kyriaki and Artusi, Alessandro and Hadjistassou, Constantinos},
journal={Marine Pollution Bulletin},



K.Kylili , C. Hadjistassou and A. Artusi

An Intelligent Way for Discerning Plastics at the Shorelines and the Seas

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.


title={An intelligent way for discerning plastics at the shorelines and the seas},
author={Kylili, Kyriaki and Hadjistassou, Constantinos and Artusi, Alessandro},
journal={Environmental Science and Pollution Research},