文献类型：期刊文献

英文题名：AHSC-Net: A Fish Pose Estimation Method for Intelligent Monitoring in Precision Aquaculture

作者：Peng, Xiaohong[1];Lu, Ronghan[1];Xiao, Zhuohan[1];Chen, Xiaohan[1,2]

机构：[1]Guangdong Ocean Univ, Coll Math & Comp Sci, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2026

卷号：11

期号：5

外文期刊名：FISHES

收录：SCI-EXPANDED(收录号：WOS:001775148300001)、、Scopus(收录号：2-s2.0-105040186399)、WOS

基金：This research was funded by the National Key R&D Program of China, grant number 2024YFC3308004; the Guangdong Intelligence Platform of Prawn Modern Seed Industry, grant number 2022GCZX001; Program for Scientific Research Start-up funds of Guangdong Ocean University, grant number 060302102305; Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, grant number 2023B1212030003; 2025 Annual Zhanjiang Science and Technology Plan Project, grant number 2025B01091 and 2025B01102; and the Research and application demonstration of key technologies for intelligent prawn breeding, grant number 2023ZDZX4012.

语种：英文

外文关键词：fish pose estimation; fish keypoint detection; fish behavior recognition; automated aquaculture

外文摘要：In aquaculture, fish physiological information serves as the foundation for behavior recognition, precise feeding, and health monitoring. The acquisition of such information relies on accurate keypoint detection and pose estimation of the fish body. To address the challenges caused by inter-occlusion among fish schools and blurred keypoint boundaries in underwater environments, a novel fish pose estimation method based on the Adaptive-kernel Hybrid-center Structural Constraint Network (AHSC-Net) is proposed. Optimized specifically for the characteristics of fish poses, the proposed method effectively enhances detection accuracy and robustness in complex underwater scenarios. First, a Stochastic Local Centroid Sampling (SLCS) strategy is introduced to improve detection capability. By simulating centroid positions in occluded samples, this approach enhances the model's ability to detect partially occluded fish. Next, a Spatial-Awareness Enhanced Pose Structural Constraint (SAPSC) is established through coordinate embedding and morphological constraints. It ensures the rationality of the predicted poses. Furthermore, an Adaptive Kernel Modulation Module (AKMM) is designed to dynamically adjust the Gaussian kernel distribution, effectively addressing challenges posed by underwater blurring and variations in fish scales. Experimental results demonstrate that AHSC-Net achieves 92.0% AP and 94.6% AR on a self-constructed largemouth bass dataset, outperforming state-of-the-art methods such as HRNet, HigherHRNet, DEKR, and YOLO-Pose. This study presents a fish pose estimation method that provides effective technical support for automated and precise monitoring in aquaculture.