Research on Automatic Dimension Detection Technology for Workpieces Based on Computer Vision

This paper presents an automated dimension detection technology for workpieces based on computer vision, utilizing straight-line fitting algorithms for analysis. The primary objective of this technology is to enhance the accuracy and efficiency of workpiece dimension measurement, thereby reducing human errors and saving time. We provide comprehensive implementation details including image preprocessing techniques, edge detection algorithms (such as Canny or Sobel operators), and Hough transform-based line fitting methods. The implementation involves key functions for coordinate extraction, least squares fitting, and dimensional calculation between parallel lines. We include complete program code (compatible with MATLAB or OpenCV Python) along with sample images for practical testing. Additionally, we discuss the technological advantages including sub-pixel accuracy, real-time processing capabilities, and potential application scenarios in industrial automation and quality control systems. This enables readers to better understand and implement this technology in various manufacturing and inspection environments.