Kylili Kyriaki, Artusi Alessandro, Hadjistassou Constantinos
Department of Engineering, Marine and Carbon Lab, University of Nicosia, 46 Makedonitissas Avenue, 2417 Nicosia, Cyprus.
DEepCamera MRG, CYENS Centre of Excellence, Constantinou Paleologou 1, Tryfon Building, 1011 Nicosia, Cyprus.
Mar Pollut Bull. 2021 Dec;173(Pt B):113127. doi: 10.1016/j.marpolbul.2021.113127. Epub 2021 Nov 10.
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/m. 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.
本文提出的智能方法基于深度学习技术构建，该技术能够识别、定位和绘制海洋环境中塑料碎片的形状。利用塞浦路斯六个海滩的塑料垃圾图像，开发的工具显示塑料垃圾密度为0.035件/米。将该结果外推至该岛的整个海岸线，智能方法估计约有66000件塑料制品，总重量约为1000千克。除了推断塑料垃圾密度外，还借助OpenCV轮廓图像处理工具确定了所有记录的塑料垃圾的尺寸。结果显示，主要物体长度在10至30厘米之间，这与经常破坏这些海岸线的常见塑料垃圾长度一致。总之，仅使用现场视觉扫描抽样调查，未采用人工收集方式来预测塑料垃圾的密度和尺寸。
