http://arxiv.org/abs/1909.11806
The 500-meter Aperture Spherical Radio Telescope(FAST) has an active reflector. During the observation, the reflector will be deformed into a paraboloid of 300-meters. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. And the way to detect the nodes in the photos is the key problem in photogrammetry. This paper applies Convolutional Neural Network(CNN) with candidate regions to detect the nodes in the photos. The experiment results show a high recognition rate of 91.5%, which is much higher than the recognition rate of traditional edge detection.
Z. Zhang, L. Zhu, W. Tang, et. al.
Fri, 27 Sep 19
36/64
Comments: 13 pages, 12 figures, 2 tables, CNN applied on FAST’s reflector measurement; matches the published version in RAA
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