基于知识蒸馏的轻量型浮游植物检测网络

doi:10.3969/j.issn.0255-8297.2020.03.003

Abstract

Abstract: Object detection framework based on convolution neural network usually uses a very deep convolution neural network to extract object features before detection. However, its huge network structure leads to the reduction of detection speed, thus, the model can hardly achieve real-time object detection and be put into embedded devices. Address to the problem, this paper applies a knowledge distillation method to feature extraction network of object detection network to improve the performance of shallow feature extraction network. In this way, the model can ensure the same performance with a big reduction on computational load and model scale. Experimental results show that the detection accuracy of feature extraction networks employing distilled shallow network is 11.7% higher than that of networks without teacher’s guidance. Moreover, we build a phytoplankton dataset in this paper, which can not only be used for the evaluation of the performance of object detection algorithms, but also will be helpful to the development of phytoplankton microscopic vision technology.

Key words: knowledge distillation, feature extraction network, phytoplankton, Faster RCNN, object detection

CLC Number:

TP391

ZHANG Tongtong, DONG Junyu, ZHAO Haoran, LI Qiong, SUN Xin. Lightweight Phytoplankton Detection Network Based on Knowledge Distillation[J]. Journal of Applied Sciences, 2020, 38(3): 367-376.

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Lightweight Phytoplankton Detection Network Based on Knowledge Distillation

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