Resumen
Accurate quantitative plankton observation is significant for biogeochemistry and environmental monitoring. However, current observation equipment is mostly shipborne, and there is a lack of long-term, large-scale, and low-cost methods for plankton observation. This paper proposes a solution to investigate plankton using a Seascan holographic camera equipped with a ?Petrel-II? underwater glider for a longer time sequence and at a larger scale. Aiming at the new challenges of low efficiency and low accuracy of holographic image processing after integrating holographic imaging systems and underwater gliders, a novel plankton data analysis method applicable to Digital Holographic Underwater Gliders (DHUG) is proposed. The algorithm has the following features: (1) high efficiency: the algorithm breaks the traditional hologram information extraction order, focusing only on the key regions in the hologram and minimizing the redundant computation; (2) high accuracy: applying the Sobel variance algorithm to the plankton in the hologram to focus the plane extraction significantly improves the focus accuracy; and (3) high degree of automation: by integrating a convolutional neural network, the algorithm achieves a fully automated analysis of the observed data. A sea test in the South China Sea verified that the proposed algorithm could greatly improve the problems of severe plankton segmentation and the low focusing accuracy of traditional information extraction algorithms. It also proved that the DHUG plankton survey has great potential.