中国海洋大学学报（自然科学版）2026，Vol.56Issue(5)：168-180,13.DOI:10.16441/j.cnki.hdxb.20240188
基于YOLOv8优化的海洋牧场海珍类生物目标检测模型
Optimization Based on YOLOv8 for Marine Ranch Valuable Marine Organisms Target Detection Model
摘要
Abstract
In response to the challenges of low detection accuracy,missed detections,and false detections in identifying valuable marine organisms in marine ranch areas,this study introduces an enhanced algorithm for detecting rare underwater marine species using the YOLOv8 model.Firstly,a new residual attention mechanism was designed and integrated into the backbone network of the YOLOv8 model to improve focus on the detailed features of underwater targets during feature extraction.Next,a bidirectional feature pyramid with adaptive feature fusion and feature selection characteristics is incorporated into the neck network to effectively combine the strong semantic information of deep feature maps with the localization information of shallow feature maps.This emphasizes the distinctions between the target and the surroundings.The experiment showed that the mean Average Precision(mAP@0.5)of the enhanced YOLOv8 model was 92.98%,which is 1.36 percentage point higher than the original YOLOv8 model.Additionally,the mean Average Precision(mAP@0.5∶0.95)was 76.71%,indicating a 3.7 percentage point improvement over the original YOLOv8 model.Compared with mainstream object detection models such as Faster RCNN,SSD,RetinaNet,YOLOv6,and YOLOv7,the improved model has shown an increase of 1.57 percentage point,1.74 percentage point,3.17 percentage point,4.68 percentage point,and 1.47 percentage point respectively in mAP@0.5.The model proposed in this paper demonstrates high detection accuracy and robust stability in complex seabed environments.It can provide technical support for the scientific management of underwater resources in marine ranches.关键词
海珍类生物/目标检测/YOLOv8/残差注意力机制/双向特征金字塔Key words
valuable marine organisms/object detection/YOLOv8/residual attention mechanism/bi-directional feature pyramid分类
信息技术与安全科学引用本文复制引用
麦仁贵,刘雯景,王骥,周涛,刘侦龙..基于YOLOv8优化的海洋牧场海珍类生物目标检测模型[J].中国海洋大学学报（自然科学版）,2026,56(5):168-180,13.基金项目
广东省普通高校重点领域新一代信息技术专项项目(2020ZDZX3008) （2020ZDZX3008）
广东省人工智能领域重点专项项目(2019KZDZX1046)资助 Supported by the New Generation Information Technology Special Project in Key Fields of General Universities in Guangdong Province(2020ZDZX3008) （2019KZDZX1046）
the Key Special Projects in the Field of Artificial Intelligence in Guangdong Province(2019KZDZX1046) （2019KZDZX1046）
