文献类型：期刊文献

英文题名：Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion

作者：Peng, Xiaohong[1];Zhou, Tianyu[1];Zhang, Ying[1,2];Zhao, Xiaopeng[1]

机构：[1]Guangdong Ocean Univ, Fac Math & Comp Sci, Zhanjiang 524088, Peoples R China;[2]Zhanjiang Bay Lab, Southern Marine Sci & Engn Guangdong Lab, Zhanjiang 524000, Peoples R China

年份：2024

卷号：24

期号：9

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001219991000001)、、EI(收录号：20242016089387)、Scopus(收录号：2-s2.0-85192832090)、WOS

基金：We would like to express our sincere appreciation for the funding provided, which enabled us to conduct this research.

语种：英文

外文关键词：smart aquaculture; deep learning; shrimp fry counting; SFCNet; multi-scale attention fusion

外文摘要：Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry.