Marine & Carbon Lab, Department of Engineering, University of Nicosia, 46 Makedonitissas Avenue, CY-2417, P.O. Box 24005, CY-1700, Nicosia, Cyprus.
MRG DeepCamera Group, RISE Ltd, Constantinou Paleologou 1, Tryfon Building, 1011, Nicosia, Cyprus.
Environ Sci Pollut Res Int. 2020 Dec;27(34):42631-42643. doi: 10.1007/s11356-020-10105-7. Epub 2020 Jul 25.
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.
无论塑料如何污染海岸线或进入海洋，它们都会对鸟类和海洋生物构成重大威胁。在这项研究中，使用人工智能工具创建了一个基于卷积神经网络架构的图像分类器，该架构利用瓶颈方法。经过训练的瓶颈方法分类器能够将在海岸线或漂浮在海面的塑料分为八个不同的类别，分别是塑料袋、瓶子、桶、食品包装、吸管、废弃网、鱼和其他物体。该深度学习方法的识别准确率达到 90%，为智能识别海岸线和海洋中的塑料提供了一种新方法。对一系列时期和批量大小的训练和测试损失和准确性结果提供了对所提出方法的可信度。分辨率灵敏度分析的结果表明，即使将图像分辨率降低 75%，预测技术仍能保持正确识别塑料的能力。智能工具，如这里建议的那样，可以替代人工对海洋中漂浮的塑料进行分类，首次揭示了我们的海滩和海洋受到污染的塑料数量的真实规模。
