Abstract
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.
Highlights
Mobile communication base station serves to transmit radio transmission and reception stations between mobile communication switching center and mobile terminal within a certain radio coverage area
This paper presented an automatic antenna parameters measurement method based on deep learning, which consumes little, engages with low hardware requirements and suits for popularization and its performance surpasses all other state-of-the-art methods
Our method emphasizes the united collaboration of Mask R-CNN, least squares, frame sequence analysis and unmanned aerial vehicle (UAV) to fully automatically measure antenna parameters, skillfully realizing multi-field cooperation of software and hardware
Summary
Mobile communication base station serves to transmit radio transmission and reception stations between mobile communication switching center and mobile terminal within a certain radio coverage area. Deep learning method was proposed by Hinton in 2006, and has gradually received attention in massive information processing, image feature extraction and prediction modeling [3]–[7] It is essentially an unsupervised layer-by-layer training method, which uses unlabeled samples for pre-learning, corrects and learns the discriminated features through a small number of labeled samples that has achieved amazing performance in object detection, segmentation and recognition. To meet industry standards and achieve high detection accuracy and fitting accuracy, an antenna database containing 19715 images named UAV-Antenna has been constructed by image capturing with UAVs flying around various base stations. These images consist of a training set of 19496 unlabeled images and a testing set of 219 labeled images. Experiments include time, detection and fitting accuracy analysis on models with varying parameters setting, and UAV-Antenna database is tested with several architectures in terms of YOLOv3, Faster R-CNN and Mask R-CNN
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