Intelligent Antenna Attitude Parameters Measurement Based on Deep Learning SSD Model

doi:10.12142/ZTECOM.2022S1006

ZTE Communications ›› 2022, Vol. 20 ›› Issue (S1): 36-43.DOI: 10.12142/ZTECOM.2022S1006

• Research Paper • Previous Articles Next Articles

Intelligent Antenna Attitude Parameters Measurement Based on Deep Learning SSD Model

FAN Guotian¹(), WANG Zhibin²

^1.ZTE Corporation, Shenzhen 518057, China
^2.Xidian University, Xi’an 710071, China

Received:2021-04-16 Online:2022-01-25 Published:2022-03-01
About author:FAN Guotian (fan.guotian@zte.com.cn) received his M.Sc. degree in network systems from Univeristy of Sunderland, UK in 2008. Currently he is working as a deputy director in ZTE Corporation, China. His research interests include big-data mining, deep learning of digital image, intelligent planning and optimization of wireless network.|WANG Zhibin received his master’s degree in computer science and technology from Xidian University, China in 2020. He is now a Ph.D. candidate of computer science and technology, Xidian University. His main research interests include database SQL engine & executor related machine learning and deep learning, spatio temporal data retrieval, data analysis, and image analysis and processing.
Supported by:
ZTE Industry?Academia?Research Cooperation Funds(HC?CN?20181030016)

Abstract

Abstract:

Due to the consideration of safety, non-contact measurement methods are becoming more acceptable. However, massive measurement will bring high labor-cost and low working efficiency. To address these limitations, this paper introduces a deep learning model for the antenna attitude parameter measurement, which can be divided into an antenna location phase and a calculation phase of the attitude parameter. In the first phase, a single shot multibox detector (SSD) is applied to automatically recognize and discover the antenna from pictures taken by drones. In the second phase, the located antennas’ feature lines are extracted and their attitude parameters are then calculated mathematically. Experiments show that the proposed algorithms outperform existing related works in efficiency and accuracy, and therefore can be effectively used in engineering applications.

Key words: deep learning, drone, object detection, SSD algorithm, visual measurement, antenna attitude parameters

FAN Guotian, WANG Zhibin. Intelligent Antenna Attitude Parameters Measurement Based on Deep Learning SSD Model[J]. ZTE Communications, 2022, 20(S1): 36-43.

Figures/Tables 12

Figure 1 Mobile-single shot multibox detector (SSD) object detection network model

Figure 2 Difference between MobileNet and traditional network

Figure 3 Feature line extracted

Figure 4 Pitch angle and azimuth angle

Figure 5 Calculation of angles

Table 1 Confusion matrix of the results

	Predicted Positive Samples	Predicted Negative Samples
Real positive samples	TP	FN
Real negative samples	FP	TN

Table 2 Comparison of results of the accurary of each model

Network Structure	Antenna Side	Antenna Top	MAP
YOLO	78.62%	74.21%	76.42%
YOLOV3	80.34%	77.85%	79.10%
original SSD	84.34%	80.52%	82.58%
Mobile-SSD	87.42%	84.78 %	86.18%

Figure 6 Comparison of results

Figure 7 Result of the pitch angle measurement experiment

Figure 8 Result of the azimuth angle measurement experiment

Figure 9 Pitch angle measurement comparison

Figure 10 Azimuth angle measurement comparison

References 9

1	MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: the communication perspective [J]. IEEE communications surveys & tutorials, 2017, 19(4): 2322–2358. DOI: 10.1109/COMST.2017.2745201 DOI URL
2	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016: 779–788. DOI: 10.1109/CVPR.2016.91 DOI URL
3	LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector [C]//European Conference on Computer Vision. ECCV, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2 DOI URL
4	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [J]. Computer science, 2014
5	HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications [EB/OL]. [2021-04-16].
6	PAO D C W, LI H F, JAYAKUMAR R. Shapes recognition using the straight line Hough transform: Theory and generalization [J]. IEEE transactions on pattern analysis and machine intelligence, 1992, 14(11): 1076–1089. DOI: 10.1109/34.166622 DOI URL
7	GVON GIOI R, JAKUBOWICZ J, MOREL J M, et al. LSD: A fast line segment detector with a false detection control [J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 32(4): 722–732. DOI: 10.1109/TPAMI.2008.300 DOI URL
8	WANG M L. Pose measurement with multiple-view space geometry [D]. Xidian University, 2017
9	ZHOU M. Attitude measurement of antenna based on the UAV aerial images [D]. Xidian University, 2019

[1]	WANG Chongchong, LI Yao, WANG Beibei, CAO Hong, ZHANG Yanyong. Point Cloud Processing Methods for 3D Point Cloud Detection Tasks [J]. ZTE Communications, 2023, 21(4): 38-46.
[2]	GONG Panyin, ZHANG Guidong, ZHANG Zhigang, CHEN Xiao, DING Xuan. Research on Fall Detection System Based on Commercial Wi-Fi Devices [J]. ZTE Communications, 2023, 21(4): 60-68.
[3]	DENG Letian, ZHAO Yanru. Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions [J]. ZTE Communications, 2023, 21(2): 11-17.
[4]	LU Ping, SHENG Bin, SHI Wenzhe. Scene Visual Perception and AR Navigation Applications [J]. ZTE Communications, 2023, 21(1): 81-88.
[5]	GAO Zhengguang, LI Lun, WU Hao, TU Xuezhen, HAN Bingtao. A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems [J]. ZTE Communications, 2022, 20(4): 110-115.
[6]	ZHANG Jintao, HE Zhenqing, RUI Hua, XU Xiaojing. Spectrum Sensing for OFDMA Using Multicarrier Covariance Matrix Aware CNN [J]. ZTE Communications, 2022, 20(3): 61-69.
[7]	HE Hongye, YANG Zhiguo, CHEN Xiangning. Payload Encoding Representation from Transformer for Encrypted Traffic Classification [J]. ZTE Communications, 2021, 19(4): 90-97.
[8]	XUE Songyan, LI Ang, WANG Jinfei, YI Na, MA Yi, Rahim TAFAZOLLI, Terence DODGSON. To Learn or Not to Learn:Deep Learning Assisted Wireless Modem Design [J]. ZTE Communications, 2019, 17(4): 3-11.
[9]	ZHENG Xiaoqing, LU Yaping, PENG Haoyuan, FENG Jiangtao, ZHOU Yi, JIANG Min, MA Li, ZHANG Ji, JI Jie. Detecting Abnormal Start-Ups, Unusual Resource Consumptions of the Smart Phone: A Deep Learning Approach [J]. ZTE Communications, 2019, 17(2): 38-43.
[10]	ZHENG Xiaoqing, CHEN Jun, SHANG Guoqiang. Deep Neural Network-Based Chinese Semantic Role Labeling [J]. ZTE Communications, 2017, 15(S2): 58-64.

Intelligent Antenna Attitude Parameters Measurement Based on Deep Learning SSD Model

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 9

Related Articles 10

Recommended Articles 0

Metrics