Research On the Balance Optimization Algorithm of Image Recognition Accuracy and Speed Based On Au-tocollimator Measurement
CSTR:
Author:
Affiliation:

Chongqing University of Post and Telecommunications

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

  • Article
  • | |
  • Metrics
  • |
  • Reference [22]
  • | |
  • Cited by
  • | |
  • Comments
    Abstract:

    The autocollimator is an important device for achieving precise, small-angle, non-contact measurements. It primarily ob-tains angular parameters of a plane target mirror indirectly by detecting the position of the imaging spot. There is limited reporting on the core algorithmic techniques in current commercial products and recent scientific research. This paper ad-dresses the performance requirements of coordinate reading accuracy and operational speed in autocollimator image posi-tioning. It proposes a cross-image center recognition scheme based on the Hough transform and another based on Zernike moments and the least squares method. Through experimental evaluation of the accuracy and speed of both schemes, the optimal image recognition scheme balancing measurement accuracy and speed for the autocollimator is determined. Among these, the center recognition method based on Zernike moments and the least squares method offers higher meas-urement accuracy and stability, while the Hough transform-based method provides faster measurement speed.

    Reference
    [1] LARICHEV R A, FILATOV Y V. A Model of Angle Measurement Using an Autocollimator and Optical Polygon[C]//Photonics. MDPI, 2023, 10(12): 1359.
    [2] Hu B, Chen W, Zhang Y, et al. Vision-based multi-point real-time monitoring of dynamic displacement of large-span cable-stayed bridges[J]. Mechanical Systems and Signal Processing, 2023, 204: 110790.
    [3] Mi C, Liu Y, Zhang Y, et al. A vision-based displacement measurement system for foundation pit[J]. IEEE Transactions on Instrumentation and Measurement, 2023.
    [4] Zhong t L, Ming y L, Jian f L. High Precision Autocollimation Measurement Technology Based on Image Recognition[C]//2021 2nd International Conference on Computing and Data Science (CDS). IEEE, 2021: 125-129.
    [5] ZHAO B, SONG Y Y, LI J W. High-Precision Real-Time Angular Measurement Method with Three Degrees of Freedom[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2023, 46 (06): 1-8.
    [6] Xu H x, Ren L x, Wu F q, et al. Laser Spot Center Recognition and Localization Algorithm for Crane Track Measurement[J]. Crane and Transport Machinery, 2023, (24): 13-20.
    [7] Lu B, Bai B, Zhao X. Vision-based structural displacement measurement under ambient-light changes via deep learning and digital image processing[J]. Measurement, 2023, 208: 112480.
    [8] Minh N Q, Huong N T T, Khanh P Q, et al. Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations[J]. Remote Sensing, 2024, 16(5): 819.
    [9] Yevsieiev V, Maksymova S, Abu-Jassar A. The Canny Algorithm Implementation for Obtaining the Object Contour in a Mobile Robot’s Workspace in Real Time[J]. 2024.
    [10] Saidani T, Ghodhbani R, Ben A M, et al. Design and Implementation of a Real-Time Image Processing System Based on Sobel Edge Detection using Model-based Design Methods[J]. International Journal of Advanced Computer Science Applications, 2024, 15(3).
    [11] Tenekeci M E, Abdulazeez S T, Karada? K, et al. Edge detection using the Prewitt operator with fractional order telegraph partial differential equations (PreFOTPDE)[J]. Multimedia Tools and Applications, 2024: 1-17.
    [12] Kong X, Yi J, Wang X, et al. Full-field mode shape identification based on subpixel edge detection and tracking[J]. Applied Sciences, 2023, 13(2): 747.
    [13] Ma J, Ren X, Tsviatkou V Y, et al. A novel fully parallel skeletonization algorithm[J]. Pattern Analysis and Applications, 2022: 1-20.
    [14] Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns[J]. Communications of the ACM, 1984, 27(3): 236-239.
    [15] Yu H. Image-type displacement measurement resolution improvement without magnification imaging[J]. Measurement Science and Technology, 2021, 33(1): 015103.
    [16] Viale L, Daga A P, Fasana A, et al. Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan[J]. Mechanical Systems and Signal Processing, 2023, 190: 110154.
    [17] Li M Z ,Shi x L ,Zhi r Z , et al.Improved YOLOv5 foreign object detection for transmission lines[J].Optoelectronics Letters,2024,20(08):490-496.
    [18] Cai S, Zhou R G, Luo J, et al. Integer multiple quantum image scaling based on NEQR and bicubic interpolation[J]. Chinese Physics B, 2024, 33(4): 040302.
    [19] Javeed M A, Ghaffar M A, Ashraf M A, et al. Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions[J]. Electronics, 2023, 12(5): 1079.
    [20] Guo L, Wu S. FPGA implementation of a real-time edge detection system based on an improved Canny algorithm[J]. Applied Sciences, 2023, 13(2): 870.
    [21] Li Y, Gan X. An integrated fast Hough transform for multidimensional data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 11365-11373.
    [22] Zhang S, Wang F, Wu X, et al. MTF measurement by slanted-edge method based on improved Zernike moments[J]. Sensors, 2023, 23(1): 509.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation
Share
Article Metrics
  • Abstract:17
  • PDF: 0
  • HTML: 0
  • Cited by: 0
History
  • Received:September 01,2024
  • Revised:September 01,2024
  • Adopted:September 09,2024
Article QR Code