...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images
【24h】

Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images

机译：挤压和激励等级更快的R-CNN用于SAR图像中的船舶检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top K values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using Fl as matric and executes 14% faster.
机译：合成孔径雷达（SAR）舰船检测是海洋监测的重要组成部分。随着计算机视觉的发展，深度学习已用于SAR图像中的船舶检测，例如基于区域的快速卷积神经网络（R-CNN），单发多盒检测器和密集连接的网络。在SAR舰船检测领域，深度学习在近海区域具有比传统方法更好的检测性能。这是因为传统方法在检测之前需要进行海陆分割，而不正确的海陆掩膜会降低其检测性能。尽管当前的深度学习SAR船舶检测方法在陆地地区仍然存在许多错误的检测，但在海域中仍遗漏了一些船舶。在这封信中，提出了一种基于更快的R-CNN的新网络架构，以通过使用挤压和激励机制来进一步提高检测性能。为了提高性能，首先，使用ImageNet预训练的VGG网络提取并连接特征图，以获得多尺度特征图。在感兴趣区域合并之后，从子特征图生成具有介于0和1之间的值的编码比例向量。比例矢量被排序，并且仅保留前K个值。其他值将设置为0。然后，通过此比例尺矢量重新校准子功能图。通过该操作将抑制冗余子特征图，并且可以提高检测器的检测性能。基于Sentinel-1图像的实验结果表明，该方法的检测性能达到0.836，比以Fl作为基质的最新方法的检测性能高9.7％，执行速度提高了14％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号