Individual Fish Recognition Method with Coarse and Fine-Grained Feature Linkage Learning for Precision Aquaculture

• Published in: Aquaculture research Vol. 2023; pp. 1 - 14
• Main Authors: Yin, Jianhao, Wu, Junfeng, Gao, Chunqi, Yu, Hong, Liu, Liang, Jiang, Zhongai, Guo, Shihao
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 26.10.2023; Hindawi Limited
• Subjects: Accuracy; Agricultural production; Aquaculture; Biometrics; Classification; Complexity; Datasets; Deep learning; Fins; Fish; Learning; Management decisions; Neural networks; Noise levels; Polyculture (aquaculture); Support vector machines; Underwater; Water quality
• ISSN: 1355-557X; 1365-2109
• DOI: 10.1155/2023/3224064

With the increasing level of precision and intelligence in the aquaculture, real-time mastery of the growth status of aquaculture individuals has become an important means to improve aquaculture efficiency and save resources and the environment. Therefore, accurate individual recognition of underwater fish has become one of the key technologies for precision aquaculture. In order to cope with the impact of the complex underwater environment on the recognition accuracy, this paper proposes a coarse and fine-grained features learning method for individual fish recognition. The method consists of a coarse-grained feature learning network and two fine-grained feature learning networks. The trunk of the network is responsible for learning coarse-grained features of the fish, the first branch learns fine-grained features of fish from head, body, and tail, and the second branch learns fine-grained features of fish from upper and lower fins. we supplemented different levels of noise and attack to the training set of fine-grained features and enriched the grayscale variation to cope with the complexity and variability of the underwater environment. The simulation experimental results show that the method achieves more than 96.7% in key indicators such as Rank-1 and Rank-5, and also performs well in other fish recognition tasks with certain generalization.