电讯技术2026，Vol.66Issue(1)：47-54,8.DOI:10.20079/j.issn.1001-893x.240918007
一种基于YOLO的轻量型多尺度船舶检测算法
A Lightweight Multi-scale Ship Detection Algorithm Based on YOLO
摘要
Abstract
To tackle the challenges of large parameter counts,complex models,and suboptimal small target detection in ship detection,a lightweight ship detection algorithm based on structural re-parameterization,called RA-YOLO,is proposed.Firstly,the RepGhost backbone network,an enhancement of Ghost that employs structural re-parameterization,is used to reduce model complexity and improve detection efficiency.Then,the Adown multi-scale feature fusion is employed to integrate deep and shallow features,thereby enhancing the ability to understand semantic cues and improving the detection accuracy of small targets.Finally,the EIoU loss function is used to optimize bounding box regression,resulting in high-quality anchor boxes and enhanced localization accuracy for target ships.The enhanced RA-YOLO demonstrates increased recall and mAP@50,achieving 83.4%and 91.1%,respectively,while reducing parameters and floating point operations per second(FLOPs)by 30.0%and 20.7%,thus improving its feasibility for deployment on resource-constrained devices.关键词
目标检测/船舶检测/结构重参数化/特征融合Key words
object detection/ship detection/structural re-parameterization/feature fusion分类
信息技术与安全科学引用本文复制引用
张开发,王玫,神显豪,唐超尘,阚瑞祥..一种基于YOLO的轻量型多尺度船舶检测算法[J].电讯技术,2026,66(1):47-54,8.基金项目
国家自然科学基金资助项目(62071135) （62071135）
广西科技重大专项(桂科AA23062035) （桂科AA23062035）
