...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS International Journal of Geo-Information >Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks
【24h】

Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks

机译：深度卷积神经网络在遥感影像中的小井盖探测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at different scales in remotely sensed images and DCNNs’ fixed receptive field cannot match the scale variability of such objects; (2) Manhole cover objects in large-scale remotely-sensed images are relatively small in size and densely packed, while DCNNs have poor localization performance when applied to such objects. To address these problems, we propose an effective method for detecting manhole cover objects in remotely-sensed images. First, we redesign the feature extractor by adopting the visual geometry group (VGG), which can increase the variety of receptive field size. Then, detection is performed using two sub-networks: a multi-scale output network (MON) for manhole cover object-like edge generation from several intermediate layers whose receptive fields match different object scales and a multi-level convolution matching network (M-CMN) for object detection based on fused feature maps, which combines several feature maps that enable small and densely packed manhole cover objects to produce a stronger response. The results show that our method is more accurate than existing methods at detecting manhole covers in remotely-sensed images.
机译：随着遥感技术的发展和高分辨率图像的出现，获取数据变得越来越方便。但是，小井盖信息的获取仍然存在人工勘测效率低，泄漏率高的缺点。最近，深度学习模型，尤其是深度卷积神经网络（DCNN），已被证明对物体检测有效。但是，一些挑战限制了DCNN在使用遥感图像进行人孔覆盖物检测中的应用：（1）人孔覆盖物在遥感图像中经常以不同的比例出现，并且DCNN的固定接收场无法匹配此类对象的比例变化; （2）大型遥感图像中的人孔盖物体尺寸相对较小且密集，而DCNN应用于此类物体时定位性能较差。为了解决这些问题，我们提出了一种有效的方法来检测遥感图像中的人孔盖物体。首先，我们通过采用视觉几何组（VGG）重新设计特征提取器，这可以增加感受野的大小。然后，使用两个子网进行检测：一个多尺度输出网络（MON），用于从几个接收层与不同目标尺度匹配的中间层生成人孔盖对象状边缘;以及一个多级卷积匹配网络（M- CMN）用于基于融合特征图的目标检测，融合了多个特征图，这些特征图使小而密的人孔盖物体能够产生更强的响应。结果表明，该方法在检测遥感图像中的人孔盖方面比现有方法更为准确。

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