集美大学学报（自然科学版）2026，Vol.31Issue(2)：176-188,13.DOI:10.19715/j.jmuzr.2026.02.05
基于改进YOLOv8n的船舶目标检测算法
Ship Target Detection Algorithm Based on Improved YOLOv8n
摘要
Abstract
To address the challenges of missed and false detections in ship image target detection tasks caused by diverse ship target scales and complex background environments,a DSAMPN-YOLOv8n algorithm model for ship target detection was proposed.The model introduces dynamic snake convolution(DSC)and sim-ple parameter-free attention mechanism to enhance the YOLOv8n network's ability to extract and integrate fea-tures in complex environments.Additionally,a multi-scale asymptotic feature pyramid network(AFPN)was constructed to facilitate multi-scale feature exchange,improving feature fusion and enhancing the model's capa-bility to detect ship targets of different scales.The proposed algorithm model was experimentally validated using the Seaships dataset.Results showed that,in comparing with those from the YOLOv8n algorithm,the optimized DSAMPN-YOLOv8n algorithm achieves improvements in precision,recall,mAP@0.5 by 6.0%,6.7%,3.6%,respectively.These findings validate the accuracy and effectiveness of the proposed method.关键词
船舶目标/目标检测/YOLOv8n/特征提取/特征融合Key words
ship targets/object detection/YOLOv8n/feature extraction/feature fusion分类
信息技术与安全科学引用本文复制引用
陈颖,周海峰,郑东强,张兴杰,黄金满..基于改进YOLOv8n的船舶目标检测算法[J].集美大学学报（自然科学版）,2026,31(2):176-188,13.基金项目
国家自然科学基金项目(51179074) （51179074）
福建省自然科学基金项目(2021J01839) （2021J01839）
集美大学安麦信产学研项目(S20127) （S20127）
