Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture

• Published in: Journal of marine science and engineering Vol. 12; no. 10; p. 1823
• Main Authors: Liu, Jintao, Tolón-Becerra, Alfredo, Bienvenido-Barcena, José Fernando, Yang, Xinting, Zhu, Kaijie, Zhou, Chao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Annotations; Aquaculture; Aquaculture industry; Automation; Correlation coefficient; Correlation coefficients; Datasets; density map; Excitation; Feature extraction; Fish; fish counting; Fish culture; Fisheries; Fishery production; Fishes; Image enhancement; Information processing; Modules; Neural networks; Swin transformer; Vision systems; China; Beijing China
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12101823

Real-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced with the Swin transformer to extract image features more efficiently. Additionally, a squeeze-and-excitation (SE) module is introduced to enhance feature representation by dynamically adjusting the importance of each channel through “squeeze” and “excitation”, making the extracted features more focused and effective. Finally, a multi-scale fusion (MSF) module is added after the back-end to fully utilize the multi-scale feature information, enhancing the model’s ability to capture multi-scale details. The experiment demonstrates that Swin-CSRNet achieved excellent results with MAE, RMSE, and MAPE and a correlation coefficient R2 of 11.22, 15.32, 5.18%, and 0.954, respectively. Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. Therefore, the proposed method not only counts the number of fish with higher speed and accuracy but also contributes to advancing the automation of aquaculture.