AUTHOR=Yue Junbai , Chen Zhenshuai , Long Yupu , Cheng Kaichang , Bi Hongsheng , Cheng Xuemin 

TITLE=Toward efficient deep learning system for in-situ plankton image recognition

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1186343

DOI=10.3389/fmars.2023.1186343

ISSN=2296-7745

ABSTRACT=Plankton is critical for the structure and function of marine ecosystems. In the past three decades, various underwater imaging systems have been developed to collect in-situ plankton images and image processing has been a major bottleneck that hinders the deployment of plankton imaging systems. In recent years, deep learning methods have greatly enhanced our ability of processing in-situ plankton images, but high computational demands and long time-consumption still remain problematic. In this study, we used knowledge distillation as a framework for model compression and improved computing efficiency while maintaining original high accuracy. A novel inter-class similarity distillation algorithm based on feature prototypes was proposed and enabled the student network (small-scale) to acquire excellent ability for plankton recognition after being guided by teacher network (large-scale). To identify the suitable teacher network, we compared emerging Transformer neural networks and convolution neural networks (CNNs), and the best performing deep learning model, Swin-B was selected. Utilizing the proposed knowledge distillation algorithm, the feature extraction ability of Swin-B was transferred to 5 more lightweight networks, and the results had been evaluated in taxonomic dataset of in-situ plankton images. Subsequently, the chosen lightweight model and the Bilateral-Sobel edge enhancement were tested to process in-situ images with high level of noises captured from coastal waters of Guangdong, China, and achieved an overall recall rate of 91.73 %. Our work contributes to effective deep learning models and facilitates the deployment of underwater plankton imaging systems by promoting both accuracy and speed in recognition of plankton targets.