基于多任务学习的近岸舰船检测方法

nshore Warship Detection Method Based on Multi-task Learning

在遥感光学图像近岸舰船目标检测任务中,针对近岸复杂场景中存在形状近似目标的虚警问题,提出一种基于多任务学习的近岸舰船目标检测方法。该方法通过构建海陆分割任务与舰船检测任务并行双路框架,将传统的任务串行处理流程优化为并行处理方式,设计联合损失函数进行双路优化训练约束,提升模型训练的稳定性,通过双分支融合模块剔除陆地掩膜中的检测结果,实现陆地虚警滤除。采用谷歌地球遥感图像制作的数据集进行实验,将本文提出的方法与单任务检测算法YOLOv5相比,mAP提升了4.4个百分点,虚警率降低了3.4个百分点。实验结果表明本文算法对陆地虚警抑制有效。

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.