In the field of measuring parameters of mobile communication base station antenna, most of its methods share some deficiencies to a different extent. The traditional ones suffer from inferior efficiency and low safety coefficient while the state-of-the-art ones reveal problems of unsatisfying operational speed and accuracy. To address these problems, a high-speed and high-precision measuring system has been proposed with AntennaNet to speed up instance segmentation. Firstly, the dual attention mechanism module is designed and adopted in AntennaNet, which can promote the accuracy and efficiency of the detection and segmentation of mobile base station antenna, while the PointRend module with down-tilt fitting is also employed to smooth the edges of masks segmented from antenna images and measure the parameters data. Secondly, transfer learning is introduced to ameliorate overfitting and training difficulty caused by small samples. Thirdly, threshold, pixel coordinates and least square are applied to ascertain the number of antennas and measure the relevant parameters. Finally, this high speed and high precision measuring system is complete with the combination of android App and UAV. Experimental results fully attest to the preponderance of the proposed method in its significant improvement of segmentation speed, fitting accuracy speed and fitting accuracy of the antenna, whose relevant parameters of the fitting also conform to industry standards. Our method not only ensures staff safety but also improves social efficiency, which revolutionized the way of measuring antenna parameters of mobile communication base stations.

AntennaNet: Antenna Parameters Measuring Network for Mobile Communication Base Station Using UAV / Y. Zhai, Q. Ke, X. Liu, W. Zhou, Y. Xu, Z. Ying, J. Gan, J. Zeng, R. Donida Labati, V. Piuri, F. Scotti. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - 70(2021). [10.1109/TIM.2021.3058980]

AntennaNet: Antenna Parameters Measuring Network for Mobile Communication Base Station Using UAV

R. Donida Labati;V. Piuri
Penultimo
;
F. Scotti
Ultimo
2021

Abstract

In the field of measuring parameters of mobile communication base station antenna, most of its methods share some deficiencies to a different extent. The traditional ones suffer from inferior efficiency and low safety coefficient while the state-of-the-art ones reveal problems of unsatisfying operational speed and accuracy. To address these problems, a high-speed and high-precision measuring system has been proposed with AntennaNet to speed up instance segmentation. Firstly, the dual attention mechanism module is designed and adopted in AntennaNet, which can promote the accuracy and efficiency of the detection and segmentation of mobile base station antenna, while the PointRend module with down-tilt fitting is also employed to smooth the edges of masks segmented from antenna images and measure the parameters data. Secondly, transfer learning is introduced to ameliorate overfitting and training difficulty caused by small samples. Thirdly, threshold, pixel coordinates and least square are applied to ascertain the number of antennas and measure the relevant parameters. Finally, this high speed and high precision measuring system is complete with the combination of android App and UAV. Experimental results fully attest to the preponderance of the proposed method in its significant improvement of segmentation speed, fitting accuracy speed and fitting accuracy of the antenna, whose relevant parameters of the fitting also conform to industry standards. Our method not only ensures staff safety but also improves social efficiency, which revolutionized the way of measuring antenna parameters of mobile communication base stations.
Antenna measurements; AntennaNet; Base stations; Dual attention mechanism; Fitting; Mobile antennas; Mobile communication; Parameters measurement; PointRend with down-tilt fitting; Task analysis; Transfer learning; Transfer learning
Settore INF/01 - Informatica
16-feb-2021
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/820175
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