Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks

Abstract

Microplastics (MPs), measuring less than 5 mm, pose threats to ecological security and human health in urban waters. Additionally, they act as carriers, transporting pollutants from terrestrial systems into oceanic circulation, contributing to global pollution. Recognizing the significance of identifying MPs in urban waters, one potential solution to the time-consuming and labor-intensive manual identification process is the application of a convolutional neural network (CNN). Therefore, having a reliable CNN model that efficiently and accurately identifies MPs is essential for extensive research on MPs pollution in urban waters. In this work, an MPs dataset with complex background was acquired from urban waters in southern China. The dataset was used to train and validate CNN models, including UNet, UNet2plus, and UNet3plus. Subsequently, the computational and inference performance of the three models was evaluated using a newly collected MPs dataset. The results showed that UNet, UNet2plus, UNet3plus, after being trained for 120 epochs, provided efficient inferences within less than 1 s, 2 s, and 3 s for 100 MPs images, respectively. Accurate segmentation with mIoU of 91.45 ± 5.93 % and 91.08 ± 6.18 % was achieved using UNet and UNet2plus, respectively, while UNet3plus exhibited a lower performance with only 82.21 ± 10.33 % mIoU. This work demonstrated that UNet and UNet2plus deliver efficient and accurate identification of MPs in urban waters. Developing CNN models that efficiently and accurately identify MPs is crucial for reducing manual time, especially in large-scale investigations of MPs pollution in urban waters.