Qian Peisheng, Zhao Ziyuan, Liu Haobing, Wang Yingcai, Peng Yu, Hu Sheng, Zhang Jing, Deng Yue, Zeng Zeng
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1954-1957. doi: 10.1109/EMBC44109.2020.9176204.
Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.
水质对工业、农业和公众健康有着直接影响。藻类物种是水质的常见指标。这是因为藻类群落对其栖息地的变化很敏感，能提供有关水质变化的宝贵信息。然而，水质分析需要在显微镜下对藻类进行专业的检测和分类检查，这非常耗时且繁琐。在本文中，我们提出了一种用于藻类检测和分类的新型多目标深度学习框架。我们在一个大规模彩色微观藻类数据集上进行了广泛的实验。实验结果表明，所提出的方法在藻类检测、类别识别和属类识别方面具有良好的性能。
