Mobile Communication Base Station Antenna Measurement Using Unmanned Aerial Vehicle

Traditional base station antenna measurement methods conducted with professional worker climbing towers tend to raise safety and inefficiency concerns in practical application. Designed to address the above problems, this paper proposes an intelligent and fully automatic antenna measurement unmanned aerial vehicle (UAV) system for mobile communication base station. Firstly, an antenna database, containing 19,715 images, named UAV-Antenna is constructed by image capturing with the help of UAVs flying around various base stations. Secondly, Mask R-CNN is adopted to train an optimal instance segmentation model on UAV-Antenna. Then, pixel coordinates and threshold are utilized for measuring antenna quantity and separate all antenna data for further measuring. Finally, a least squares method is employed for measuring antenna parameters. Experimental results show that the proposed method can not only satisfy the industry application standards, but also guarantee safety of labors and efficiency of performance.

[1]  Raimondo Schettini,et al.  Evaluating CNN-Based Semantic Food Segmentation Across Illuminants , 2019, CCIW.

[2]  Haihong Hu,et al.  Instance Segmentation of Auroral Images for Automatic Computation of Arc Width , 2019, IEEE Geoscience and Remote Sensing Letters.

[3]  Thomas F. Eibert,et al.  A Crane-Based Portable Antenna Measurement System—System Description and Validation , 2019, IEEE Transactions on Antennas and Propagation.

[4]  A. Ferragina,et al.  Bayesian and partial least square global forage calibrations models developed by an iterative procedure using R , 2019, Proceedings of the 18th International Conference on Near Infrared Spectroscopy.

[5]  Zhenxue Chen,et al.  Fast Semantic Segmentation for Scene Perception , 2019, IEEE Transactions on Industrial Informatics.

[6]  Korra Sathya Babu,et al.  Face expression recognition system based on ripplet transform type II and least square SVM , 2019, Multimedia Tools and Applications.

[7]  Wei Xiang,et al.  ThunderNet: A Turbo Unified Network for Real-Time Semantic Segmentation , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[8]  Dong Xu,et al.  Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection , 2019, IEEE Transactions on Image Processing.

[9]  Boqing Gong,et al.  A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Keshav Kumar.  Discrete Wavelet Transform (DWT) Assisted Partial Least Square (PLS) Analysis of Excitation-Emission Matrix Fluorescence (EEMF) Spectroscopic Data Sets: Improving the Quantification Accuracy of EEMF Technique , 2018, Journal of Fluorescence.

[11]  Rahil Garnavi,et al.  Deep Semantic Instance Segmentation of Tree-Like Structures Using Synthetic Data , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[12]  Eamonn J. Keogh,et al.  Domain agnostic online semantic segmentation for multi-dimensional time series , 2018, Data Mining and Knowledge Discovery.

[13]  Nishchal K. Verma,et al.  Object Detection and Recognition Using Small Labeled Datasets , 2018, Advances in Intelligent Systems and Computing.

[14]  Deyu Meng,et al.  Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework , 2018, International Journal of Computer Vision.

[15]  Xiao Xiang Zhu,et al.  Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Trung-Nghia Le,et al.  Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[18]  Yuri Álvarez López,et al.  On the Use of Unmanned Aerial Vehicles for Antenna and Coverage Diagnostics in Mobile Networks , 2018, IEEE Communications Magazine.

[19]  Kiyoharu Aizawa,et al.  Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Mohsen Guizani,et al.  Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges , 2018, IEEE Access.

[21]  Arun Kumar Sangaiah,et al.  Face expression recognition system based on ripplet transform type II and least square SVM , 2017, Multimedia Tools and Applications.

[22]  Stephan ten Brink,et al.  Deep Learning Based Communication Over the Air , 2017, IEEE Journal of Selected Topics in Signal Processing.

[23]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[24]  Garrison W. Cottrell,et al.  Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[25]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[27]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[28]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[31]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, Computer Vision and Pattern Recognition.

[32]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[33]  Sun-Yuan Kung,et al.  Salient Object Detection via Fuzzy Theory and Object-Level Enhancement , 2019, IEEE Transactions on Multimedia.

[34]  Borja Gonzalez-Valdes,et al.  Unmanned Aerial System for Antenna Measurement (UASAM) , 2018 .

[35]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..