当前位置:
X-MOL 学术
›
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-05-21 , DOI: 10.1109/jstars.2021.3082526 Mingming Zhu , Guoping Hu , Hao Zhou , Shiqiang Wang , Yule Zhang , Shijie Yue , Yu Bai , Kexin Zang
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-05-21 , DOI: 10.1109/jstars.2021.3082526 Mingming Zhu , Guoping Hu , Hao Zhou , Shiqiang Wang , Yule Zhang , Shijie Yue , Yu Bai , Kexin Zang
Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew nonmaximum suppression. Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.
中文翻译:
基于RetinaNet的遥感图像任意定向船舶检测
针对船舶检测中任意方位、大纵横比、密集排列等问题,提出了一种基于RetinaNet的任意方位船舶检测方法。我们提出的方法包括旋转的 RetinaNet、改进的网络、特征对齐模块和改进的损失函数。首先,旋转的 RetinaNet 通过使用特征金字塔网络、旋转的锚点、偏斜交叉联合(IoU)和偏斜非极大值抑制来实现旋转检测。然后,引入细化网络和特征对齐模块以实现更好的检测精度。最后,为了解决边界不连续性,通过引入 IoU 常数因子来改进损失函数。考虑到 HRSC2016 数据集存在的问题,我们建立了一个具有更准确标签和更多图像和对象样本的新数据集。通过消融研究,我们彻底分析了所提出的旋转 RetinaNet、特征对齐模块和改进的损失函数的有效性。实验结果表明,我们的方法优于其他最先进的方法。
更新日期:2021-07-16
中文翻译:
基于RetinaNet的遥感图像任意定向船舶检测
针对船舶检测中任意方位、大纵横比、密集排列等问题,提出了一种基于RetinaNet的任意方位船舶检测方法。我们提出的方法包括旋转的 RetinaNet、改进的网络、特征对齐模块和改进的损失函数。首先,旋转的 RetinaNet 通过使用特征金字塔网络、旋转的锚点、偏斜交叉联合(IoU)和偏斜非极大值抑制来实现旋转检测。然后,引入细化网络和特征对齐模块以实现更好的检测精度。最后,为了解决边界不连续性,通过引入 IoU 常数因子来改进损失函数。考虑到 HRSC2016 数据集存在的问题,我们建立了一个具有更准确标签和更多图像和对象样本的新数据集。通过消融研究,我们彻底分析了所提出的旋转 RetinaNet、特征对齐模块和改进的损失函数的有效性。实验结果表明,我们的方法优于其他最先进的方法。