End-to-End Plankton Database Collection System

Abstract

Monitoring living plankton status has greatly developed owing to machine vision technology. One of these developments is the automatic video acquisition system, which can capture high-resolution information and record considerable details of a scene. However, existing optical sensors cannot usually obtain a whole image, in which all details of plankton are fully clear, due to the influence of many factors, such as depth-of-field limitation of lenses, movement of plankton, and large difference in plankton scale. The captured video also needs to be filtered to eliminate the redundancy before using for the sparsity of the sample. This procedure is time-consuming and costly. Therefore, in this article, we develop an end-to-end plankton database collection system that can directly generate complete, clear plankton images from video. First, the regions of interest of the plankton are extracted. Then, the same plankton which appears in successive frames is identified to reconstruct the clearest morphological and structure features through their detailed information. Experimental results indicate that the proposed system can effectively compress the original data. The proposed fusion method also outperforms the state-of-the-art methods, especially for images with anisotropic blur. Furthermore, this system can monitor the abundance and distribution of marine plankton with the help of an embedded computing platform.