吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (1): 162-173.doi: 10.13229/j.cnki.jdxbgxb20211217
• 计算机科学与技术 • 上一篇
摘要:
为解决现有目标检测方法在检测无人机航拍图像中的交通目标时存在的水平包围框与目标真实轮廓贴合度较差、目标的水平包围框重叠度高导致相互抑制、目标发生旋转时,常规卷积操作的采样点落于目标之外等问题,在单阶段目标检测网络YOLOv3的基础上,提出了一种基于锚框变换的单阶段旋转目标检测网络(ATB-YOLO)。特征提取网络部分,设计了新的特征提取网络Darknet-53-Dense,使用Mish激活函数代替Leaky ReLU激活函数,并借鉴DenseNet网络使用拼接模块代替残差模块。针对检测头部网络,本文提出了一种锚框变换网络 (ATN),将初始的水平锚框变换为旋转锚框;并提出锚框对齐卷积 (AAC),在旋转锚框的指导下调整卷积操作的采样位置,提取锚框对齐特征图预测目标的旋转包围框和类别。实验证明,使用本文提出的特征提取网络进行检测,网络的检测精度提高了1.38%;本文提出的锚框对齐卷积AAC,相比常规卷积、可变卷积和锚框指导可变卷积检测精度分别提高了4.38%、4.24%和3.79%;与几种主流的旋转目标检测方法进行对比,本文方法在获得了与二阶段检测器相当的精度的同时,达到了21.2帧/s的准实时检测速度。
中图分类号:
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