Gold crucian carp identification based on Siamese network

Houcheng Su; Weiyao Kong; Kailin Jiang; Dan Liu; Xinyao Gong; Bin Lin; Jiao Li; Hongjie Wang; Chao Xu

doi:10.1117/12.2611630

22 October 2021 Gold crucian carp identification based on Siamese network

Houcheng Su, Weiyao Kong, Kailin Jiang, Dan Liu, Xinyao Gong, Bin Lin, Jiao Li, Hongjie Wang, Chao Xu

Proceedings Volume 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021); 119280V (2021) https://doi.org/10.1117/12.2611630
Event: International Conference on Image Processing and Intelligent Control (IPIC 2021), 2021, Lanzhou, China

Abstract

At present, with the improvement of the quality of life, people's requirements for the quality of food are also gradually improved. The most effective way to effectively supervise the quality of food is to carry out food traceability supervision. Fish can provide people with abundant protein and excellent taste, and it has gradually been favored by people. However, due to the complex underwater environment, RFID technology, which is often used for pigs, cattle and other animals, is not suitable for large-scale fishery breeding, and it cannot be effectively traced to the source of fish food, which is difficult for the government and consumers to supervise. Using deep learning method to identify animals can avoid a large amount of labor costs and effectively trace the source of fish. Taking golden crucian carp as an example, this paper proposed to use YOLOV5 to detect the position of golden crucian carp and cut it out, and applied Siamese Network to carry out identification, and the identification accuracy reached 89.8%, which provided research ideas for the identification technology of fish.

Citation Download Citation

Houcheng Su, Weiyao Kong, Kailin Jiang, Dan Liu, Xinyao Gong, Bin Lin, Jiao Li, Hongjie Wang, and Chao Xu "Gold crucian carp identification based on Siamese network", Proc. SPIE 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021), 119280V (22 October 2021); https://doi.org/10.1117/12.2611630

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KEYWORDS

Gold

Facial recognition systems

Target detection

Data modeling

Performance modeling

Machine vision

Neural networks

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