文献类型：期刊文献

英文题名：GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish

作者：Liu, Mingxin[1,2];Zhang, Chun[1];Lin, Cong[1,2]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Guangdong Prov Key Lab Inteligent Equipment South, Zhanjiang, Peoples R China

年份：2025

卷号：12

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001491999900001)、、Scopus(收录号：2-s2.0-105005780669)、WOS

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This work was partly supported by the National Natural Science Foundation of China (62171143), Guangdong Provincial University Innovation Team (2023KCXTD016), special projects in key fields of ordinary universities in Guangdong, Province (2021ZDZX1060), the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604SSJS00301), the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2024011, the Open Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (Grant NO. 2023B1212030003), Guangdong Ocean University Technology Achievement Transformation Project Team (JDTD2024003).

语种：英文

外文关键词：deep learning; abnormal behavior detection; greater amberjack; aquaculture; YOLOv8; real-time monitoring

外文摘要：With the growing global population and economic development, the demand for sashimi has increased, presenting both new opportunities and challenges for aquaculture. As a key species for sashimi, Greater Amberjack faces significant potential in aquaculture but is also vulnerable to temperature fluctuations, particularly during its juvenile stage, which can lead to abnormal behaviors. These behavioral anomalies, if undetected, can impede growth and result in substantial economic losses. Traditional methods for detecting abnormal behavior rely heavily on manual inspection, a process that is time-consuming and labor-intensive. Meanwhile, existing automated detection algorithms often struggle with a trade-off between detection accuracy and model size. To address this issue, we propose a precise and lightweight model for detecting Greater Amberjack's abnormal behaviors, based on the YOLOv8n architecture (named GAB-YOLO). First, we introduce the SobelMaxDS module, designed to enhance the network's ability to extract edge and spatial features, thereby enabling more effective capture of the fish's behavioral contours and preserving rich target information. This enhancement improves the model's robustness against challenges such as image blurring, occlusion, and false detections in complex environments. Additionally, the PMSRNet module is integrated into the backbone network to replace C2f, improving the model's feature extraction capabilities through multi-scale feature fusion and enhanced spatial information capture, which aids in the accurate localization of the fish target.Furthermore, by incorporating shared decoupled heads for classification and regression features, alongside GroupConv and DBB(Diverse Branch Block) modules in the detection head, we significantly reduce the model's parameter count while simultaneously improving its accuracy and robustness. Finally, the introduction of the Wise-ShapeIoU loss function further accelerates the model's convergence and optimization process. Experimental results demonstrate that, compared to the original model, the number of parameters and FLOPs are reduced by 36.7% and 28.4%, respectively, while the Precision is increased by 5.1%. The model achieves a detection speed of 172 frames per second, outperforming other mainstream detection models. This study addresses the real-time detection requirements for Greater Amberjack's abnormal behaviors in aquaculture and offers considerable practical value for fish farming operations.