Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning

The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reconstruction of digital holography require a strictly controlled laboratory environment and time-consuming iterative computation, respectively, which impede its application in marine plankton imaging. In this paper, an intelligent method designed with digital holography and deep learning algorithms is proposed to detect and recognize marine plankton (IDRMP). An accurate integrated A-Unet network is established under the principle of deep learning and trained by digital holograms recorded with publicly available plankton datasets. This method can complete the work of reconstructing and recognizing a variety of plankton organisms stably and efficiently by a single hologram, and a system interface of YOLOv5 that can realize the task of the end-to-end detection of plankton by a single frame is provided. The structural similarities of the images reconstructed by IDRMP are all higher than 0.97, and the average accuracy of the detection of four plankton species, namely, Appendicularian, Chaetognath, Echinoderm and Hydromedusae,, reaches 91.0% after using YOLOv5. In optical experiments, typical marine plankton collected from Weifang, China, are employed as samples. For randomly selected samples of Copepods, Tunicates and Polychaetes, the results are ideal and acceptable, and a batch detection function is developed for the learning of the system. Our test and experiment results demonstrate that this method is efficient and accurate for the detection and recognition of numerous plankton within a certain volume of space after they are recorded by digital holography.