Visual detection of milling surface roughness based on improved YOLOV5

Authors

  • Xiao Lv School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  • Huaian Yi School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  • Runji Fang School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  • Shuhua Ai School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  • Enhui Lu School of Mechanical Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China

DOI:

https://doi.org/10.24425/mms.2023.146425

Keywords

surface roughness, improved Yolov5, detection speed, attentional mechanisms

Abstract

Workpiece surface roughness measurement based on traditional machine vision technology faces numerous problems such as complex index design, poor robustness of the lighting environment, and slow detection speed, which make it unsuitable for industrial production. To address these problems, this paper proposes an improved YOLOv5 method for milling surface roughness detection. This method can automatically extract image features and possesses higher robustness in lighting environments and faster detection speed. We have effectively improved the detection accuracy of the model for workpieces located at different positions by introducing Coordinate Attention (CA). The experimental results demonstrate that this study’s improved model achieves accurate surface roughness detection for moving workpieces in an environment with light intensity ranging from 592 to 1060 lux. The average precision of the model on the test set reaches 97.3%, and the detection speed reaches 36 frames per second.

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Published

2023-09-30

How to Cite

Lv, Xiao, et al. “Visual Detection of Milling Surface Roughness Based on Improved YOLOV5”. Metrology and Measurement Systems, vol. 30, no. 3, Sept. 2023, pp. 531-48, doi:10.24425/mms.2023.146425.

Issue

Vol. 30 No. 3 (2023)

Section

Articles