DMSNet: A Dynamic Multi-Scale Feature Fusion Segmentation Network for Precise Large Yellow Croaker Recognition in Complex Underwater Conditions

Abstract

Accurate monitoring of fish morphology and behavior is crucial to intelligent aquaculture management, especially for economically important species such as the large yellow croaker. To address challenges including turbid water, uneven lighting, and occlusions in real farming environments, this study develops DMSNet—an underwater image segmentation model based on an enhanced TransNeXt architecture. The model incorporates three novel modules, a Convolutional Dynamic Gated Linear Unit (CDGLU), an Agentic Cross-Attention Fusion Module (ACAF), and Pooling Channel-Spatial Attention (PCSA), significantly improving feature fusion and robustness under complex conditions. To better support applications in aquaculture, a dedicated dataset of underwater large yellow croaker, called the Large Yellow Croaker Dataset (LYCD), was constructed, covering the varied clarity levels typically found in farming operations. Experimental results show that DMSNet achieves an Acc of 98.01%, an IoU of 91.73%, an F1 of 96.17%, and an inference speed of 29.25 FPS, outperforming state-of-the-art methods, particularly in turbid and low-contrast scenarios. This study presents a practical and efficient tool for non-invasive fish monitoring that is capable of accurately identifying large yellow croaker underwater in real-world aquaculture environments with complex water conditions.