Fry Counting Method in High-Density Culture Based on Image Enhancement Algorithm and Attention Mechanism

• Published in: IEEE access 2024, Vol.12, p.41734-41749
• Main Authors: Chen, Hongyuan, Cheng, Yuan, Dou, Yu, Tan, Huachao, Yuan, Guihong, Bi, Hai, Liu, Dan
• Format: Article
• Language: English
• Subjects: Algorithms; Annotations; Aquaculture; attention mechanism; Datasets; Deep learning; Density measurement; Estimation; Farming; Feature extraction; Fish; fry counting; Generative adversarial networks; High density; Image enhancement; Modeling; Monitoring; Numerical analysis; Production; super resolution; Superresolution
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3365585

It is important in production to achieve accurate counting and density estimation of high-density culture fry under the environmental conditions of aquaculture scenarios in an efficient and accurate manner. However, none of the current methods for fry counting works well under the high-density and high-overlap conditions of real aquaculture scenarios. Therefore, in this paper, we propose a high-density farming fry monitoring network model, Super-Resolution GAN Density Estimate Attention Network (SGDAN), which incorporating an image enhancement algorithm and an attention mechanism, and we create a high-density farming fry dataset (HD-FryDataset) based on the environmental conditions of real aquaculture scenarios. The network model is designed to improve and optimize the targeted subnetworks for several key aspects of high-density fish fry monitoring work. Four subnetworks are included for image optimization, feature extraction, attention, and density map estimation. The experimental results show that the SGDAN network model achieved an average counting accuracy of 97.57% on the high-density culture fry dataset, which was 8.23% and 2.06% higher than those of MCNN and CSRNet, respectively. Additionally, the MAE and RMSE of the model were reduced by 71.9% and 67.3% and by 34.3% and 33.2% compared with those of MCNN and CSRNet, respectively. The model proposed in this paper also has a better ability to generate predictive density maps. The density maps generated by SGDAN have values of the evaluation metrics PSNR and SSIM of 20.33 and 0.933, respectively, which are 3.31 and 0.037 and 2.63 and 0.031 higher than those of MCNN and CSRNet. In general, the network model proposed in this paper outperforms existing network models in two applications: accurate counting of fry and generation of density maps for high-density culture in aquaculture. It also provides a good solution for digitizing the number of fry and visualizing the density of high-density culture in intelligent aquaculture systems.