Study of a Visual Measurement System for Woven Grids

Abstract

In order to solve the problems of low efficiency and poor accuracy in measuring the geometrical parameters of braided pipe mesh by traditional methods, an improved method for measuring the geometrical parameters of braided pipe is proposed. The method acquires the braided pipe grid image by a CMOS camera and reduces the influence of color and noise on the image quality by using preprocessing means such as grayscaling and bilateral filtering. Subsequently, the region of interest of the woven tube grid structure is extracted from the background using an iterative method, and the average filament diameter is calculated by Canny edge detection and a modified Hough transform. Next, the skeleton wire segment contours are obtained using the skeleton operator, and the contours are further optimized by the RDP algorithm to retain only the straight line segments. The co-linear straight line segments are connected to form a complete mesh line by a contour merging operation. Finally, the mesh size is indirectly obtained using the improved least-squares method based on feature classification. Through experimental verification, the method has a practical value as it improves the average wire diameter accuracy by 3% and the mesh size accuracy by 1% compared with the traditional machine vision measurement method.

Received: 18 February 2025 | Revised: 9 April 2025 | Accepted: 21 April 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Hufeng Zou: Conceptualization, Methodology, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Quanyu Wu: Validation, Formal analysis, Resources, Writing – review & editing, Project administration. Zehan Ye: Investigation, Resources, Writing – original draft, Writing – review & editing. Jiaqi Fan: Software. Lingjiao Pan: Writing – review & editing. Xiaojie Liu: Supervision.