BONE NAIL SIZE MEASUREMENT SYSTEM BASED ON MACHINE VISION

Authors

  • Piao Yan Zhuhai Huaxing Intelligent Manufacturing Technology Co., Ltd, Zhuhai 519000, China
  • Yang Hao Zhuhai Huaxing Intelligent Manufacturing Technology Co., Ltd, Zhuhai 519000, China and School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
  • Tan Yulong School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
  • Zheng Zehan Zhuhai Huaxing Intelligent Manufacturing Technology Co., Ltd, Zhuhai 519000, China and School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
  • Yang Jingcheng Zhuhai Huaxing Intelligent Manufacturing Technology Co., Ltd, Zhuhai 519000, China

DOI:

https://doi.org/10.29121/ijetmr.v12.i1.2025.1523

Keywords:

Machine Vision, Line Detection, Halcon, Shape Matching

Abstract

Aiming at the problems of low measurement efficiency and poor accuracy consistency in the traditional measurement methods of medical bone nails, a set of bone nail size measurement system based on machine vision was developed. Firstly, the edge position of the bone nail was located by setting the ROI area. Then the Canny algorithm is used to extract the edge accurately, and the edge points are screened by threshold, and the edge points are fitted into straight line segments by least square method for size measurement. Finally, using the Halcon image software platform, the shape matching method was used to match and locate the bone nails, and the batch detection of bone nails was realized. The experimental results show that the measurement accuracy of the system is up to ±0.01mm, the average detection time of a single picture is 54ms, the measurement accuracy is high, the running speed is fast, and it can meet the demand of industrial real-time detection.

Downloads

Download data is not yet available.

References

CAO Hao-peng, ZENG Wei-ming, & SHI Yu-hu (2018). Power Line Detection Based on Hough Transform and Total Least Squares Method [J]. Computer Technology and Development,28(10):164-167.

Chen Xiaoxin, Jiang Zhansi, Piao Yan, Yang Jingcheng, Zheng Hongxin , Yang Hao & Chen Kequan(2024). SF-Yolov8n: A Novel Ultra Lightweight and High-precision Model for Detecting Surface Defects of Dental Nails [J]. IEEE Sensors Journal, 24(12): 20103-20113. https://doi.org/10.1109/JSEN.2024.3392674

HE Wei,FAN Yubo, & LI Xiaoming (2018). Recent Research Progress of Bioactivity Mechanism and Application of Bone Repair Materials[J]. Chinese Journal of Reparative and Reconstructive Surgery, 32(09): 1107-1115.

Jiao Bo,Liu Guoning,& Zhao Mengxuan (2022). Flange Size Measurement Method Based on Machine Vision with Sub-Pixel Precision [J]. Modern Manufacturing Engineering, (07):121-126.DOI:10.16731/j.cnki.1671-3133.2022.07.019.

LI Yong-jing, ZHU Ping-yu, & SUN Xiao-peng (2017). Shape Defect Detection Algorithm of Stamping Parts Based on Shape Template Matching [J]. Journal of Guangzhou University(Natural Science Edition),16(05):62-66.

Nie Peng, Wang Jiaming, & Guo Yongyi (2022). Research on Countersink Hole Depth Detection Method Based on Machine Vision [J]. Tool Engineering,56(09):134-139.

REN Yong-qiang, TU De-jiang, & HAN Shu (2020). Dimension Measurement of Diesel Cylinder Liner Based on Machine Vision [J]. Modular Machine Tool & Automatic Manufacturing Technique,(09):151-153.

Song Shuaishuai,Huang Feng, & Jiang Yanbin (2021). Analysis on the Research progress of Geometric Measurement Technology Based on Machine Vision[J]. Electronic Measurement Technology, (03): 22-26.

YU Bo, WU Jing, & ZHOU Qi-bin (2022). An Edge Detection Algorithm Based on Improved Canny Operator [J]. Manufacturing Automation, 44(08):24-26+43.

Yang Guihua, Tang Weiwei, & Lu Pengpeng (2021). Chip Pin Measurement and Defect Detection System Based on Machine Vision [J]. Electronic Measurement Technology,44(18):136-142.DOI:10.19651/j.cnki.emt.2107535.

ZHAO Qi-wei,LI Yong-qiao, & XIE Song-le (2020). Research on Visual Measurement Methodfor Geometric Parameters of Hole Group in Riveted Thin Plate [J]. Machinery Design & Manufacture,(09):158-161.

Zhang C, & Zhang J.(2013).On-line tool wear Measurement for Ball-End Milling Cutter Based on Machine vision[J].Computers in Industry,64 (6): 708-719. https://doi.org/10.1016/j.compind.2013.03.010

Zhang Congcong, & Mu Li (2020). Research on Image Edge Detection Algorithm Based on Machine Vision [J]. Foreign Electronic Measurement Technology,39(12):80-85.DOI:10.19652/j.cnki.femt.2002269.

Zheng Hongxin, Chen Xiaoxin, Cheng Hao, Du Yixian, & Jiang Zhansi (2024). MD-YOLO: Surface Defect Detector for Industrial Complex Environments [J]. Optics and Lasers in Engineering (178) :108170. https://doi.org/10.1016/j.optlaseng.2024.108170

Zheng Ruxin,Sun Qingyun, & Xiao guodong (2021). Research on Workpiece Dimension Measurement Based on Machine Vision [J]. Electronic Measurement Technology,44(16): 110-115.DOI:10.19651/j.cnki.emt.2107065.

Downloads

Published

2025-01-24

How to Cite

Yan, P., Hao, Y., Yulong, T., Zehan, Z., & Jingcheng, Y. (2025). BONE NAIL SIZE MEASUREMENT SYSTEM BASED ON MACHINE VISION. International Journal of Engineering Technologies and Management Research, 12(1), 14–27. https://doi.org/10.29121/ijetmr.v12.i1.2025.1523