GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish - Mingxin Liu; Mingxin Liu; Chun Zhang; Cong Lin; Cong Lin (Frontiers in Marine Science)
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.