Multi-Target Deep Learning for Algal Detection and Classification
This work addresses the need for automated water quality monitoring, which impacts industry, agriculture, and public health, but it appears incremental as it applies deep learning to a specific domain without broad methodological breakthroughs.
The paper tackles the problem of time-consuming and tedious manual algal detection and classification for water quality analysis by proposing a novel multi-target deep learning framework, achieving promising performance on detection, class identification, and genus identification as demonstrated through extensive experiments on a large-scale colored microscopic algal dataset.
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.