A Lightweight Fish Detection Method for Complex Underwater Scenes

Fish observation is a key component of marine ecological monitoring and is valuable for understanding ecological processes and fish population dynamics. In practical applications, observation equipment is often constrained by limited memory and computational resources, making it difficult to deploy visual detection models with large parameter counts and high computational complexity. Under limited computational resources, existing deep-learning-based fish detection models struggle to balance detection accuracy, model lightweighting, and real-time edge deployment. To address this issue, a lightweight GEM-YOLOv8n model based on YOLOv8n is proposed for fish detection. For high model complexity, insufficient feature representation, and limited bounding box regression accuracy in complex underwater observation scenarios, the model replaces C2f modules and some Conv modules with GhostC2f and GhostConv, introduces the EMA attention mechanism, and adopts MPDIoU loss instead of CIoU. Experimental results show that, compared with YOLOv8n, GEM-YOLOv8n improves Precision, Recall, mAP50, and mAP50–95 by 0.53%, 2.16%, 0.52%, and 0.34% while reducing parameters and FLOPs by 48.0% and 39.5%. These results demonstrate that the proposed model improves detection performance while reducing model complexity. Tests on Jetson Xavier NX demonstrate real-time performance and deployment feasibility, providing a lightweight deployment solution for resource-constrained underwater fish detection.