首页 > 论文 > 光学学报 > 38卷 > 7期(pp:728001--1)

改进区域卷积神经网络的机场检测方法

Airport Detection Method with Improved Region-Based Convolutional Neural Network

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

提出了一种结合级联的区域建议网络和检测网络的遥感图像机场检测方法。通过改进区域建议网络,以获得高质量的机场建议框;通过改进检测网络的损失函数,以提高机场检测的准确性;使用交叉优化策略,实现了两个网络的卷积层共享,机场检测时间大幅缩减。结果表明,所提方法在复杂背景下能准确地检测出不同类型的机场,检测率高,虚警率低,平均运行时间短。

Abstract

An airport detection method based on remote sensing images which combines the cascaded regional proposal network with the detection network is proposed. The regional proposal network is improved to get the airport proposal boxes with a high quality, and the loss function of the detection network is improved to increase the accuracy of the airport detection. In addition, the alternating optimization strategy is adopted to share the convolution layers between the two networks, and thus the airport detection time is greatly shortened. The results show that this proposed method can be used to accurately detect different types of airports under complex backgrounds with a high detection rate, a low false-alarm rate and short average processing time.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP183;TP751.1

DOI:10.3788/aos201838.0728001

所属栏目:遥感与传感器

基金项目:国家自然科学基金(61372167,61379104)、航空科学基金(20175896022)

收稿日期:2018-01-03

修改稿日期:2018-01-23

网络出版日期:--

作者单位    点击查看

朱明明:空军工程大学研究生院, 陕西 西安 710038
许悦雷:空军工程大学研究生院, 陕西 西安 710038
马时平:空军工程大学研究生院, 陕西 西安 710038
唐红:空军工程大学研究生院, 陕西 西安 710038
辛鹏:空军工程大学研究生院, 陕西 西安 710038
马红强:空军工程大学研究生院, 陕西 西安 710038

联系人作者:许悦雷(649014294@qq.com)

备注:朱明明(1992-),男,硕士研究生,主要从事图像处理和计算机视觉方面的研究。E-mail: ming_paper@163.com

【1】Song M Z, Qu H S, Jin G. Weak ship target detection of noisy optical remote sensing image on sea surface[J]. Acta Optica Sinica, 2017, 37(10): 1011004.
宋明珠, 曲宏松, 金光. 含噪光学遥感图像海面弱小舰船目标检测[J]. 光学学报, 2017, 37(10): 1011004.

【2】Zhu D, Wang B, Zhang L, et al. Airport target detection in remote sensing images: A new method based on two-way saliency[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1096-1100.

【3】Qu L, Wang K R, Chen L L, et al. Fast road detection based on RGBD images and convolutional neural network[J]. Acta Optica Sinica, 2017, 37(10): 1010003.
曲磊, 王康如, 陈利利, 等. 基于RGBD图像和卷积神经网络的快速道路检测[J]. 光学学报, 2017, 37(10): 1010003.

【4】Zhang P, Niu X, Dou Y, et al. Airport detection from remote sensing images using transferable convolutional neural networks[C]. International Joint Conference on Neural Network, 2016: 2590-2595.

【5】Zhang P, Niu X, Dou Y, et al. Airport detection on optical satellite images using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1183-1187.

【6】Xiao Z F, Gong Y P, Long Y, et al. Airport detection based on a multiscale fusion feature for optical remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1469-1473.

【7】Xin P, Xu Y L, Ma S P, et al. Airport detection combining region proposal networks and adaptive pooling networks[J]. Journal of Xidian University, 2018(3): 121-127.
辛鹏, 许悦雷, 马时平, 等. 区域提取网络结合自适应池化网络的机场检测[J]. 西安电子科技大学学报, 2018(3): 121-127.

【8】Ye G L, Sun S Y, Gao K J, et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(8): 081003.
叶国林, 孙韶媛, 高凯珺, 等. 基于加速区域卷积神经网络的夜间行人检测研究[J].激光与光电子学进展, 2017, 54(8): 081003.

【9】Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

【10】Girshick R B. Fast R-CNN[C]. International Conference on Computer Vision, 2015: 1440-1448.

【11】Yan J, Yu Y, Zhu X, et al. Object detection by labeling superpixels[C]. Computer Vision and Pattern Recognition, 2015: 5107-5116.

【12】Kuo W, Hariharan B, Malik J, et al. DeepBox: Learning objectness with convolutional network[C]. International Conference On Computer Vision, 2015: 2479-2487.

【13】Rothe R, Guillaumin M, Van Gool L, et al. Non-maximum suppression for object detection by passing messages between windows[C]. Asian Conference on Computer Vision, 2014: 290-306.

【14】Qu Y, Li C, Zheng N, et al. Airport detection base on support vector machine from a single image[C]. International Conference on Signal Processing, 2005: 546-549.

【15】Tao C, Tan Y, Cai H, et al. Airport detection from large IKONOS images using clustered SIFT keypoints and region information[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(1): 128-132.

引用该论文

Zhu Mingming,Xu Yuelei,Ma Shiping,Tang Hong,Xin Peng,Ma Hongqiang. Airport Detection Method with Improved Region-Based Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(7): 0728001

朱明明,许悦雷,马时平,唐红,辛鹏,马红强. 改进区域卷积神经网络的机场检测方法[J]. 光学学报, 2018, 38(7): 0728001

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF