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A rapid, low-cost deep learning system to classify squid species and evaluate freshness based on digital images

文献类型： 外文期刊

作者： Hu, Jun 1 ; Zhou, Chengquan 1 ; Zhao, Dandan 1 ; Zhang, Linnan 2 ; Yang, Guijun 3 ; Chen, Wenxuan 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Food Sci Inst, Hangzhou, Zhejiang, Peoples R China

2.Ningbo Frirui Marine Biotechnol Co Ltd, Ningbo, Zhejiang, Peoples R China

3.Minist Agr PR China, Key Lab Quantitat Remote Sensing Agr, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Squid species classification; Freshness evaluation; Deep learning; Low-cost system

期刊名称：FISHERIES RESEARCH （ 影响因子：2.422； 五年影响因子：2.594 ）

ISSN： 0165-7836

年卷期： 2020 年 221 卷

页码：

收录情况： SCI

摘要： We developed and evaluated a rapid, low-cost system to classify squid in industrial production. This involved designing an easy-to-use handheld image-acquisition system combined with an automated, labor-saving, and efficient deep learning model (named "improved faster recurrent convolutional neural network") to identify three squid species from the North Pacific Ocean. Three indicators, Accuracy, Intersection-over-Union, and Average Running Time, are used to evaluate the classification, and the average results for the test samples are 85.7%, 80.1%, and 0.144 s, respectively. The proposed network provides better squid classification compared with four other approaches. In addition, to ensure quality, the freshness of the selected squid is also evaluated using global threshold segmentation analysis. This proposed method is demonstrated to be a robust, noninvasive, and high-throughput system for squid classification and can also be expanded to other fine processing of aquatic products.

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