现代电子技术2025，Vol.48Issue(5)：79-85,7.DOI:10.16652/j.issn.1004-373x.2025.05.013
基于YOLOv8n的轻量化鱼类检测算法
Lightweight fish detection algorithm based on YOLOv8n
摘要
Abstract
At present,the fishes in polar region are monitored and investigated by combining sonar detection with artificial fishing statistics.However,this method is limited by economic cost,operation area and time.Object detection algorithms based on deep learning can identify and detect fishes while meeting economic requirements.In the traditional object detection algorithms,however,there are a large number of parameters and large calculation quantity,so the algorithms fail to adapt to the harsh conditions of energy consumption and storage limitations in the polar region.In view of this,an improved lightweight fish detection algorithm based on YOLOv8n is proposed.In the algorithm,the GhostC2f module is used to replace C2f in the backbone and neck networks,and GhostConv is used to replace part of the Conv in the network,and the EMA is introduced in the backbone network to improve the feature extraction ability.Finally,the loss function MPDIoU,which has a simpler calculation,is used to replace the CIoU to improve the detection speed.Experiments on the self-made fish dataset show that the number of parameters and computation burden of the improved algorithm become 1.49×106 and 4.7×109,respectively,and only 49.67% of the parameters of the original YOLOv8n are used to achieve a detection accuracy slightly higher than that of the YOLOv8n.When the proposed algorithm is deployed in the embedded Jetson Xavier NX,it can achieve an inspection speed of up to 47 f/s,so it can provide technical support for fish detection in hardware-constrained situations.关键词
鱼类检测/YOLOv8n/轻量化/极区/声呐探测/EMA注意力机制Key words
fish detection/YOLOv8n/lightweight/polar region/sonar detection/EMA分类
信息技术与安全科学引用本文复制引用
王明慧,陈燕,寇立伟,窦银科..基于YOLOv8n的轻量化鱼类检测算法[J].现代电子技术,2025,48(5):79-85,7.基金项目
国家重点研发计划(2022YFC2807603) （2022YFC2807603）
山西省基础研究计划资助项目(202203021211175,202103021223048) （202203021211175,202103021223048）
