The Five-hundred-meter Aperture Spherical radio Telescope(FAST) has an active reflector.During observations, the reflector will be deformed into a paraboloid 300 meters in diameter. To improve its surface accuracy, we...The Five-hundred-meter Aperture Spherical radio Telescope(FAST) has an active reflector.During observations, the reflector will be deformed into a paraboloid 300 meters in diameter. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. The way to detect the nodes in the photos is the key problem in this application of photogrammetry. This paper applies a convolutional neural network(CNN) with candidate regions to detect the nodes in the photos. Experimental results show a high recognition rate of 91.5%, which is much higher than the recognition rate for traditional edge detection.展开更多
基金supported by study on the fusion of total station dynamic tracking measuring and IMU inertial measuring for the feed support measurement in FAST (Grant No. 11503048)the Open Project Program of the Key Laboratory of FAST, NAOC, Chinese Academy of Sciencesthe Key Laboratory of Radio Astronomy, Chinese Academy of Sciences
文摘The Five-hundred-meter Aperture Spherical radio Telescope(FAST) has an active reflector.During observations, the reflector will be deformed into a paraboloid 300 meters in diameter. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. The way to detect the nodes in the photos is the key problem in this application of photogrammetry. This paper applies a convolutional neural network(CNN) with candidate regions to detect the nodes in the photos. Experimental results show a high recognition rate of 91.5%, which is much higher than the recognition rate for traditional edge detection.
