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

Frontiers in Marine Science

Marine Science

Xuemin Cheng¹  Junbai Yue¹  Zhenshuai Chen¹  Kaichang Cheng¹  Yupu Long¹  Hongsheng Bi² 

[1] Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China;University of Maryland Center for Environmental Science, Solomons, MD, United States;

关键词: in-situ;    image processing;    knowledge distillation;    model deployment;    deep learning;   

DOI  :  10.3389/fmars.2023.1186343

 received in 2023-03-14, accepted in 2023-08-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

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 longtime 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 the 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 five 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.

【 授权许可】

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Copyright © 2023 Yue, Chen, Long, Cheng, Bi and Cheng