  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.
