计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230700166-7.doi: 10.11896/jsjkx.230700166
陈天鹏, 胡建文
CHEN Tianpeng, HU Jianwen
摘要： 由于遥感图像中舰船目标方向任意,基于深度学习的通用目标检测算法采用水平框,在检测舰船时易框选大量背景,检测效果欠佳。文中提出一种改进全卷积一阶段目标检测网络(FCOS)的遥感图像舰船目标检测算法,以FCOS为基线,在检测头部分增加一条偏移回归分支,通过偏移水平框的上边中点和右边中点,产生旋转框。舰船目标通常具有较大的长宽比,预测框与真实框之间的角度偏差对交并比的影响较大,进而影响模型的检测精度。针对该问题,在计算偏移损失时引入与舰船目标长宽比有关的加权因子,使得具有较大长宽比的目标获得较大的偏移损失。在HRSC2016数据集上的实验结果表明,所提算法的平均精确度达到89.00%,检测速度达到19.8FPS,相比同类型的无锚框算法,其在检测速度和检测精度上均表现优秀。
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