微塑料(MPs),测量小于5毫米,城市水域生态安全和人类健康面临威胁。此外,他们充当载体,将污染物从陆地系统输送到海洋环流中,造成全球污染。认识到在城市水域识别国会议员的重要性,一个潜在的解决方案,耗时和劳动密集型的人工识别过程是卷积神经网络(CNN)的应用。因此,有一个可靠的CNN模型,有效和准确地识别国会议员是必不可少的广泛研究国会议员污染在城市水域。在这项工作中,从中国南方的城市水域获得了具有复杂背景的MPs数据集。数据集用于训练和验证CNN模型,包括UNet,UNet2plus,和UNet3plus。随后,使用新收集的MP数据集评估了三个模型的计算和推理性能.结果表明,UNet,UNet2plus,UNet3plus,在接受了120个时代的训练后,在不到1秒的时间内提供有效的推论,2s,100名议员的图像为3秒,分别。使用UNet和UNet2plus实现了mIoU为91.45±5.93%和91.08±6.18%的精确分割,分别,而UNet3plus的性能较低,仅为82.21±10.33%。这项工作表明,UNet和UNet2plus可以有效和准确地识别城市水域中的国会议员。开发有效,准确地识别国会议员的CNN模型对于减少人工时间至关重要,特别是在对城市水域中MP污染的大规模调查中。
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.