渔业现代化2025，Vol.52Issue(6)：123-136,14.DOI:10.26958/j.cnki.1007-9580.2025.06.015
基于LSD-YOLO的水下养殖鱼类的检测和跟踪方法
Detection and tracking of underwater farmed fish based on LSD-YOLO
摘要
Abstract
A detection model LSD-YOLO based on improved YOLOv11n is proposed is proposed to address the problem of underwater aquaculture fish due to occlusion,image degradation,and difficulty in realizing accurate tracking of the fish.Firstly,a DynamicHead is introduced to give the model the ability to fuse task awareness,scale awareness,and spatial awareness.Second,a lightweight feature extraction module,LiteODSE,has been designed to combine dynamic convolution and channel attention to enhance the feature extraction capability in the backbone network.Then,the SDI multilevel feature fusion module is introduced,which can separate and fuse multi-scale spatial information.Moreover,the GIOU loss function is used instead of the CIOU loss function,and the difficult localization problem under small targets as well as non-overlapping regions can be improved by introducing the constraint information outside the bounding box.Finally,tracking accuracy is effectively improved by combining it with StrongSORT,which is currently a more advanced tracking algorithm.Experiments demonstrate that the accuracy of the designed model is improved by 3.2%and mAP50 by 3%compared with YOLOv11n.Compared with YOLOv11n+StrongSORT,the MOTA is improved by 5.2%and the number of ID switching is reduced by 30%,which proves that the improved method can be better applied in target detection and tracking of underwater farmed fish.关键词
YOLOv11n/StrongSORT/目标检测/目标跟踪Key words
YOLOv11n/StrongSORT/target detection/target tracking分类
农业科技引用本文复制引用
徐培东,梅海彬,袁红春..基于LSD-YOLO的水下养殖鱼类的检测和跟踪方法[J].渔业现代化,2025,52(6):123-136,14.基金项目
国家自然科学基金"基于海洋大数据深度学习的渔情预测模型研究(4177614)" （4177614）
