We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Plankton Detection Method Based on Neural Networks and Digital Holographic Imaging.
- Authors
Lang, Kaiqi; Cai, Hui; Wang, Xiaoping
- Abstract
Detecting marine plankton by means of digital holographic microscopy (DHM) has been successfully deployed in recent decades; however, in most previous studies, the identification of the position, shape, and size of plankton has been neglected, which may negate some of the advantages of DHM. Therefore, the procedure of image fusion has been added between the reconstruction of initial holograms and the final identification, which could help present all the images of plankton clearly in a volume of seawater. A new image fusion method called digital holographic microscopy-fully convolutional networks (DHM-FCN) is proposed, which is based on the improved fully convolutional networks (FCN). The DHM-FCN model runs 20 times faster than traditional image fusion methods and suppresses the noise in the holograms. All plankton in a 2 mm thick water body could be clearly represented in the fusion image. The edges of the plankton in the DHM-FCN fusion image are continuous and clear without speckle noise inside. The neural network model, YOLOv4, for plankton identification and localization, was established. A mean average precision (mAP) of 97.69% was obtained for five species, Alexandrium tamarense, Chattonella marina, Mesodinium rubrum, Scrippsiella trochoidea, and Prorocentrum lima. The results of this study could provide a fast image fusion method and a visual method to detect organisms in water.
- Subjects
DIGITAL holographic microscopy; HOLOGRAPHY; IMAGE fusion; ARTIFICIAL neural networks; SPECKLE interference; MARINE plankton
- Publication
Chemosensors, 2022, Vol 10, Issue 6, p217
- ISSN
2227-9040
- Publication type
Article
- DOI
10.3390/chemosensors10060217