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.
@article{arxiv.2005.03232,
title = {Multi-Target Deep Learning for Algal Detection and Classification},
author = {Peisheng Qian and Ziyuan Zhao and Haobing Liu and Yingcai Wang and Yu Peng and Sheng Hu and Jing Zhang and Yue Deng and Zeng Zeng},
journal= {arXiv preprint arXiv:2005.03232},
year = {2022}
}
Comments
Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada