The Line Scan Camera Calibration Based on Space Rings Group | Zendy

Menghui Niu | Zendy; Kechen Song | Zendy; Xin Wen | Zendy; Defu Zhang | Zendy; Yunhui Yan | Zendy

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The Line Scan Camera Calibration Based on Space Rings Group

Author(s) -

Menghui Niu,

Kechen Song,

Xin Wen,

Defu Zhang,

Yunhui Yan

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2817629

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In addition to the accuracy requirements, the calibration of the line scan camera (LSC) should also have strong operability and portability. This paper presents a low-cost high-reliability LSC calibration method. First, a 3D right-angle stereo target based on concentric rings is designed, which is named space rings group, and it is suitable for static calibration of LSC. The calibration target has a simple structure and is easy to carry. The calibration process does not require other auxiliary instruments, greatly improving the operability of the calibration for the line camera. Second, in order to improve the positioning accuracy of subpixel edges in the image and reduce the computational complexity, a subpixel edge detection method is adopted, which is a region-by-region localization algorithm, a combination of global adaptive Canny detection and local partial area effect (Local-Canny-PAE, LCPAE). Then, the relationship between the actual spatial point and the image point is established by using the invariant nature of the image and the imaging model of the camera, and the two-step particle swarm optimization (TS_PSO) algorithm is used to solve the internal parameters of the camera. Finally, the validity of this method is verified by computer simulation and real experiment.

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