Intelligent Antenna Attitude Parameters Measurement based on Deep Learning SSD Model
FAN Guotian1, WANG Zhibin2
(1. ZTE Corporation, Shenzhen 518052, China;
2. Xidian University, Xi'an 710071, China)
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 parameters 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.
deep learning; drone; object detection; SSD algorithm; visual measurement; antenna attitude parameters