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Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning

文献类型： 外文期刊

作者： Liu, Shijing 1 ; Qian, Cheng 1 ; Tu, Xueying 1 ; Zheng, Haojun 3 ; Zhu, Lin 1 ; Liu, Huang 1 ; Chen, Jun 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China

2.Ocean Univ China, Sanya Oceanog Inst, Sanya 572011, Peoples R China

3.Dalian Ocean Univ, Sch Nav & Naval Architecture, Dalian 116023, Peoples R China

关键词： fish recognition; CycleGAN; foreground mask loss; maximum mean discrepancy; transfer learning

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.9； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 11 卷 7 期

页码：

收录情况： SCI

摘要： Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. To solve these problems, we proposed a new method for identifying large yellow croaker based on the CycleGAN (cycle generative adversarial network) and transfer learning. This method constructs source sample sets and target sample sets by acquiring large yellow croaker images in controllable scenes and actual farming conditions, respectively. The CycleGAN was used as the basic framework for image transformation from the source domain to the target domain to realize data amplification in the target domain. In particular, IDF (identity foreground loss) was used to optimize identity loss judgment criteria, and MMD (maximum mean discrepancy) was used to narrow the distribution between the source domain and target domain. Finally, transfer learning was carried out with the expanded samples to realize the identification of large yellow croaker under varying conditions. The experimental results showed that the proposed method achieved good identification results in both the controlled scene and the actual culture scene, with an average recognition accuracy of 96.9% and 94%, respectively. These provide effective technical support for the next steps in fish behavior tracking and phenotype measurement.

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