红外技术2026，Vol.48Issue(2)：146-155,10.
面向无人艇自主航行下的水面检测模型
Water Surface Detection Model for Autonomous Navigation of Unmanned Vessels
摘要
Abstract
To address the problems of missed and false detections of surface targets by unmanned vessels operating in complex multi-scenario water environments,a surface object detection model based on improved YOLOv8s is proposed.First,the small-target detection layer is reconstructed by introducing low-level feature details to reduce the number of model parameters and improve the model's perception of small targets.Second,partial convolution(PConv)is introduced to replace the traditional Conv and construct the feature extraction module P-C2f,aiming to reduce redundant features and computation,which further compress the model size.Subsequently,the reparameterized generalized feature pyramid network(RepGFPN)is used to fuse features,aiming to enhance the full interaction and fusion of low-level detail information and high-level semantic information,thereby improving the multiscale target detection ability of the model.Finally,transfer learning is used to fine-tune the model and further improve detection performance.When tested on the WSODD dataset,the reference number of the improved model decreased by nearly 67.5%compared to that of the original model,whereas recall rate R increased by 4%,and mAP@0.5 increased by 2.1%,reaching 81.4%.Compared with other mainstream detection models,the improved model has obvious advantages and can help unmanned vehicles perform better surface detection tasks.关键词
水面检测/YOLOv8/重构的小目标检测层/P-C2f/RepGFPN/迁移学习Key words
water surface detection/YOLOv8/reconstructed small object detection layer/P-C2f/RepGFPN/transfer learning分类
信息技术与安全科学引用本文复制引用
翟杰祥,袁宏武,李恋..面向无人艇自主航行下的水面检测模型[J].红外技术,2026,48(2):146-155,10.基金项目
国家自然科学基金资助项目(61906118,62273001),安徽省自然科学基金资助项目(2108085MF230). （61906118,62273001）
