Machine vision-based sorting method for precision industrial components

Hongming Li; Ye Ding; Mingyu Shao

doi:10.1117/12.3041660

29 August 2024 Machine vision-based sorting method for precision industrial components

Hongming Li, Ye Ding, Mingyu Shao

Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 132490H (2024) https://doi.org/10.1117/12.3041660
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China

Abstract

With the rapid development of industrial automation, automated sorting of precision industrial components has become the key to improve production efficiency and reduce costs. In this paper, a machine vision-based sorting method for precision industrial components is proposed, which can effectively recognize and classify iron sheets of different shapes and sizes. This study innovatively integrates image processing, pattern recognition and machine vision technologies to design an efficient automated sorting process. Designed for industrial precision components, this method integrates image preprocessing techniques optimized for industrial-grade image acquisition environments, accurate feature extraction methods for efficient identification of key component attributes, and advanced contour analysis and customized shape matching algorithms to ensure fast and accurate classification of all types of industrial components in automated sorting processes.The experimental results show that the proposed algorithm has significant advantages in improving the sorting accuracy and efficiency, and provides a new gripping and sorting technical solution for the industrial automation field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongming Li, Ye Ding, and Mingyu Shao "Machine vision-based sorting method for precision industrial components", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 132490H (29 August 2024); https://doi.org/10.1117/12.3041660

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