改进的深度神经网络下遥感机场区域目标检测

韩永赛; 马时平; 何林远; 李承昊; 朱明明; 许悦雷

doi:10.13700/j.bh.1001-5965.2020.0225

改进的深度神经网络下遥感机场区域目标检测

doi: 10.13700/j.bh.1001-5965.2020.0225

1.
空军工程大学航空工程学院, 西安 710038
2.
西北工业大学无人系统技术研究院, 西安 710072

基金项目:

国家自然科学基金 61701524

国家自然科学基金 61773397

航空科学基金 20175896022

详细信息

通讯作者:
马时平, E-mail: 1013765061@qq.com

中图分类号: TP183;TP751.1
计量
- 文章访问数: 643
- HTML全文浏览量: 152
- PDF下载量: 145
- 被引次数: 0
出版历程
- 收稿日期: 2020-05-28
- 录用日期: 2020-08-21
- 网络出版日期: 2021-07-20

Regional object detection of remote sensing airport based on improved deep neural network

1.
School of Aeronautical Engineering, Air Force Engineering University, Xi'an 710038, China
2.
Institute of Unmanned Systems Technology, Northwestern Polytechnical University, Xi'an 710072, China

Funds:

National Natural Science Foundation of China 61701524

National Natural Science Foundation of China 61773397

Aeronautical Science Foundation of China 20175896022

More Information

Corresponding author: MA Shiping, E-mail: 1013765061@qq.com

摘要

摘要:
卫星遥感监测器下的机场区域多类目标检测在实际生活中有着重大的军用和民用意义。为了有效提升机场区域遥感图片的检测精确率，以主流目标检测方法中更快的区域卷积神经网络（Faster R-CNN）为基础框架，针对数据侧提出了ReMD数据增强算法。同时使用更具深度的残差神经网络（ResNet）以及特征融合部件-特征金字塔网络（FPN）来提取机场区域目标更鲁棒的深层区分性特征。在末端检测网络添加新的全连接层并根据目标的类间关联性组合softmax分类器以及4个logistic regression分类器进行机场区域多类目标的精确分类。实验结果表明：相比原网络改进后的网络带来了11.6%的多类平均检测精确率的提升，达到了80.5%的mAP，与其他主流网络进行对比也有更好的精确率；同时通过适当减小建议区域的输入量，可以在降低3.2%精确率的前提下将0.512 s的检测时间提速3倍，至0.173 s，根据具体任务可以合理权衡精确率和检测速度，体现了该网络的有效性以及实用性。
- 目标检测 /
- 图像处理 /
- 遥感 /
- 机场区域 /
- 神经网络
Abstract:
The detection of multiple types of targets in the airport area under the satellite remote sensing monitor is of great military and civilian significance in real life. In order to effectively improve the detection accuracy of remote sensing images in the airport area, based on the representative deep network Faster R-CNN in the mainstream target detection method, the ReMD data enhancement algorithm is proposed for the data side. The deep ResNet network and the feature fusion component-FPN are used to extract more robust deep distinguishing features of airport area target. Finally, a new fully connected layer is added to the end detection network, and the softmax classifier and 4 logistic regression classifiers are combined to accurately classify airport area multi-class targets according to the target class correlation. Experiments show that the improvement of the original network brings a 11.6% increase in the average detection accuracy rate of the original network, reaching 80.5% mAP. Compared with other mainstream networks, it also has a better accuracy rate. At the same time, by appropriately reducing the input amount of the recommended area, under the premise of 3.2% reduction of accuracy rate, the detection time of 0.512 s is improved by 3 times to 0.173 s. According to the specific task, the accuracy and detection speed can be reasonably weighed, which reflects the effectiveness and practicability of the network.
- object detection /
- image processing /
- remote sensing /
- airport area /
- neural network

HTML全文

图 1 机场区域目标检测示意图

Figure 1. Schematic diagram of airport area target detection

下载: 全尺寸图片幻灯片

图 2 “跳层连接”示意图

Figure 2. Schematic diagram of "layer jump connection"

下载: 全尺寸图片幻灯片

图 3 FPN特征融合检测示意图

Figure 3. Schematic diagram of FPN feature fusion detection

下载: 全尺寸图片幻灯片

图 4 数据集示意图

Figure 4. Schematic diagram of data set

下载: 全尺寸图片幻灯片

图 5 ReMD效果示意图

Figure 5. Schematic diagram of ReMD effect

下载: 全尺寸图片幻灯片

图 6 新型末端检测器示意图

Figure 6. Schematic diagram of new end detector

下载: 全尺寸图片幻灯片

图 7 机场区域目标检测效果示意图

Figure 7. Schematic diagram of object detection effect in airport area

下载: 全尺寸图片幻灯片

图 8 对比实验结果示意图

Figure 8. Schematic diagram of comparative experiment results

下载: 全尺寸图片幻灯片

表 1 目标-标签对应表

Table 1. Object-label correspondence table

目标	airport	civil airplane	transport plane	fighter	helicopter	bridge	oil tank
标签	airport	airplane_mh	airplane_y	airplane_z	airplane_zs	bridge	oil tank

下载: 导出CSV

表 2 目标-类别号对应表

Table 2. Object-number correspondence table

目标标签	airplane	airport airport	bridge bridge	oil tank oil tank	civil airplane airplane_mh
类别号	1	2	3	4	5
目标	transport plane	fighter	helicopter	background
标签	airplane_y	airplane_z	airplane_zs
类别号	6	7	8	0

下载: 导出CSV

表 3 各数据增强算法效果

Table 3. Effect of each data enhancement algorithm

数据增强算法	各算法使用情况
Spin	√	√	√	√	√	√	√	√
Mirror		√	√	√	√	√	√	√
Scaling			√	√	√	√	√	√
Pan				√	√	√	√	√
Brightness Change					√	√	√	√
Crop						√	√	√
Gaussian Noise							√	√
ReMD(proposed)								√
mAP/%	68.9	69.4	69.8	69.9	70.4	71.1	71.3	72.6

下载: 导出CSV

表 4 基础网络对比实验结果

Table 4. Comparison of experiment results of basic network

模型	mAP/%	Average IOU
ZFNet	58.4	0.392
VGG_CNN_M_1024	63.5	0.425
VGG-16	72.6	0.566
VGG-19	73.9	0.571
ResNet-50	74.1	0.572
ResNet-101	75.8	0.574
ResNet-50+FPN	78.9	0.643
ResNet-101+FPN	80.2	0.645

下载: 导出CSV

表 5 加入新型末端检测器前后对比实验结果

Table 5. Comparison of experiment results before and after adding the new end detector

网络	AP/%							mAP/%
网络	airport	bridge	oil tank	Civil airplane	Transport plane	fighter	helicopter	mAP/%
T1	89.7	75.8	76.9	89.6	78.5	72.0	78.9	80.2
T2	89.8	75.9	76.9	90.1	79.1	72.6	79.4	80.5

下载: 导出CSV

表 6 各检测部件与所带来的时间成本

Table 6. Summary of each testing component and time cost

Faster R-CNN网络及增加的部件	原网络	ReMD	ResNet-101	FPN	N2	N1
检测时间/s	0.215	0.215	0.483	0.510	0.512	0.217
Δt/s	0	0	0.268	0.027	0.002	0.002

下载: 导出CSV

表 7 不同检测网络间的对比实验结果

Table 7. Comparison of experiment results between different detection networks

算法	mAP/%	检测时间/s
R-CNN	54.2	＞10.000
SPP-Net	54.9	0.401
Faster R-CNN	68.9	0.215
HyperNet	72.0	0.160
R-FCN	75.2	0.167
YOLOv3	76.3	0.122
SSD	73.9	0.165
Ref.[30]	80.2	0.602
Ref.[16]	70.3	0.215
proposed	80.5	0.512
proposed^*	77.3	0.173
注：proposed^*表示将RPN末端生成的proposal数量减少到50(原设为300)。

下载: 导出CSV

参考文献(30)

[1]	RICHARDS J R. Remote sensing digital image analisis[M]. Berlin: Springer, 1999: 20-21.
[2]	杨四海, 陈锻生, 谢维波. Hough变换的特性分析: 一种全局观点[J]. 计算机辅助设计与图形学学报, 2006, 18(8): 1197-1204. doi: 10.3321/j.issn:1003-9775.2006.08.020 YANG S H, CHEN D S, XIE W B. Characteristics of hough transform: A global view[J]. Journal of Computer-Aided Design & Computer Graphics, 2006, 18(8): 1197-1204(in Chinese). doi: 10.3321/j.issn:1003-9775.2006.08.020
[3]	梁浩然. 自然图像的视觉显著性特征分析与检测方法及其应用研究[D]. 杭州: 浙江工业大学, 2016: 16-17. LIANG H R. Research on saliency detection of natural image and its application[D]. Hangzhou: Zhejiang University of Technology, 2016: 16-17(in Chinese).
[4]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. doi: 10.1109/TPAMI.2017.2699184
[5]	LI W, XIANG S M, WANG H B, et al. Robust airplane detection in satellite images[C]//2011 18th IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2011: 2821-2824.
[6]	林煜东, 和红杰, 尹忠科, 等. 基于稀疏表示的可见光遥感图像飞机检测算法[J]. 光子学报, 2014, 43(9): 196-201. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201409039.htm LIN Y D, HE H J, YIN Z K, et al. Airplane detection in optical remote sensing image based on sparse-representation[J]. Acta Photonica Sinica, 2014, 43(9): 196-201(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201409039.htm
[7]	仇建斌, 李士进, 王玮. 角点与边缘信息相结合的遥感图像飞机检测新方法[J]. 微电子学与计算机, 2011, 28(9): 214-216. https://www.cnki.com.cn/Article/CJFDTOTAL-WXYJ201109057.htm QIU J B, LI S J, WANG W. A new approach to detect aircrafts in remote sensing images based on corner and edge information fusion[J]. Microelectronics & Computer, 2011, 28(9): 214-216(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WXYJ201109057.htm
[8]	AN Z Y, SHI Z W, TENG X C, et al. An automated airplane detection system for large panchromatic image with high spatial resolution[J]. Optik, 2014, 125(12): 2768-2775. doi: 10.1016/j.ijleo.2013.12.003
[9]	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. doi: 10.1109/LGRS.2017.2673118
[10]	ZHU T H, LI Y H, YE Q K, et al. Integrating saliency and ResNet for airport detection in large-size remote sensing images[C]//20172nd International Conference on Image, Vision and Computing (ICIVC). Piscataway: IEEE Press, 2017: 20-25.
[11]	CHEN X Y, XIANG S M, LIU C L, et al. Aircraft detection by deep belief nets[C]//20132nd IAPR Asian Conference on Pattern Recognition. Piscataway: IEEE Press, 2013: 54-58.
[12]	WU H, ZHANG H, ZHANG J F, et al. Fast aircraft detection in satellite images based on convolutional neural networks[C]//2015 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE Press, 2015: 4210-4214.
[13]	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. doi: 10.1109/TPAMI.2016.2577031
[14]	戴陈卡, 李毅. 基于Faster RCNN以及多部件结合的机场场面静态飞机检测[J]. 计算机应用, 2017, 37(S2): 85-88. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2017S2021.htm DAI C K, LI Y. Aeroplane detection in static aerodrome based on Faster RCNN and multi-part model[J]. Journal of Computer Applications, 2017, 37(S2): 85-88(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2017S2021.htm
[15]	朱明明, 许悦雷, 马时平, 等. 基于特征融合与软判决的遥感图像飞机检测[J]. 光学学报, 2019, 39(2): 71-77. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201902009.htm ZHU M M, XU Y L, MA S P, et al. Airplane detection based on feature fusion and soft decision in remote sensing images[J]. Acta Optica Sinica, 2019, 39(2): 71-77(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201902009.htm
[16]	CHEN F, REN R L, VAN DE VOORDE T, et al. Fast automatic airport detection in remote sensing images using convolutional neural networks[J]. Remote Sensing, 2018, 10(3): 443. doi: 10.3390/rs10030443
[17]	RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252. doi: 10.1007/s11263-015-0816-y
[18]	ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[M]//Computer Vision-ECCV 2014. Berlin: Springer, 2014: 818-833.
[19]	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 121-124.
[20]	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2015: 1-9.
[21]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 770-778.
[22]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 936-944.
[23]	EVERINGHAM M, GOOL L, WILLIAMS C K I, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338. doi: 10.1007/s11263-009-0275-4
[24]	REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. (2018-04-08)[2019-07-18]. https://arxiv.org/abs/1804.02767.
[25]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 580-587.
[26]	HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. doi: 10.1109/TPAMI.2015.2389824
[27]	KONG T, YAO A B, CHEN Y R, et al. HyperNet: Towards accurate region proposal generation and joint object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 845-853.
[28]	DAI J F, LI Y, HE K M, et al. R-FCN: Object detection via region-based fully convolutional networks[C]//Neural Information Processing Systems. Piscataway: IEEE Press, 2016: 379-387.
[29]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[M]//Computer Vision-ECCV 2016. Berlin: Springer, 2016: 21-37. doi: 10.1007/978-3-319-46448-0_2
[30]	SHRIVASTAVA A, SUKTHANKAR R, MALIK J, et al. Beyond skip connections: Top-down modulation for object detection[C]//Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 256-266.