Paper Title:
The Application of Close-Range Digital Photogrammetry Based on the Neural Network in Crack-Monitoring
  Abstract

Based on the two-dimensional Direct Linear Transformation (DLT) principle of close-range digital photogrammetry and mathematical principle of the linear neuron, the equivalent relationship between linear neural network and the two-dimensional DLT of close-range digital photogrammetry is discussed. A neural network with 2 linear neurons, 6 input parameters and 2 output parameters is established to simulate the two-dimensional DLT. The network can be trained using a set of grid points in the control coordinate system with known world coordinates and pixel coordinates. The weights and biases of trained network contain camera interior and exterior parameters. A new digital photographic technique is put forward combined camera self-calibration based on neural network with non-linear pixel coordinates correction of lens distortion. The indoor survey test indicates that measurement is more accuracy. Meanwhile, the new technology is successfully used in crack monitor of a bridge pier.

  Info
Periodical
Edited by
Xuejun Zhou
Pages
2875-2880
DOI
10.4028/www.scientific.net/AMM.90-93.2875
Citation
M. M. Li, J. G. Chen, Y. X. Zhang, "The Application of Close-Range Digital Photogrammetry Based on the Neural Network in Crack-Monitoring", Applied Mechanics and Materials, Vols. 90-93, pp. 2875-2880, 2011
Online since
September 2011
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