计算机与现代化Issue(3)：29-33,5.DOI:10.3969/j.issn.1006-2475.2024.03.005
基于多任务学习的近岸舰船检测方法
Inshore Warship Detection Method Based on Multi-task Learning
摘要
Abstract
In the task of inshore warship detection in remote sensing optical images,this paper proposes an inshore warship de-tection method based on multi-task learning for the false alarms problem of similar features in complex scenes.By constructing a parallel dual-branch task framework for the sea-land segmentation mission and the warship detection mission,this method opti-mizes the traditional task of serial processing into parallel processing mode.Secondly,we propose a joint loss constraint for dual path optimum training,which improves the stability of model training.Finally,the dataset made by Google Earth remote sensing images is used for experiments.The detection results in land mask are eliminated by the dual-branch fusion model,and the land false alarm filter is realized.Compared with the single task detection algorithm YOLOv5,the mAP of the proposed method in-creased by 4.4 percentage points and the false alarm rate decreased by 3.4 percentage points.The experimental results show that the proposed algorithm is effective in suppressing false alarm on land.关键词
舰船检测/海陆分割/多任务学习/损失函数Key words
warship detection/sea-land segmentation/multi-task learning/loss function分类
信息技术与安全科学引用本文复制引用
刘馨嫔,王洪,赵良瑾..基于多任务学习的近岸舰船检测方法[J].计算机与现代化,2024,(3):29-33,5.基金项目
国家自然科学基金资助项目(62201550) （62201550）
