<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.</p>
</abstract>

The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

Research on the application of machine vision in the design of machine automation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical...

Research on the application of machine vision in the design of machine automation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 16. Feb. 2024

Akzeptiert: 19. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1420

Schlüsselwörter
Machine vision, Machine automation, Binocular camera calibration, Averaging method, Feature point extraction

© 2024 Dawei Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the application of machine vision in the design of machine automation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 16. Feb. 2024

Akzeptiert: 19. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1420

SchlüsselwörterMachine vision, Machine automation, Binocular camera calibration, Averaging method, Feature point extraction

© 2024 Dawei Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Machine vision, Machine automation, Binocular camera calibration, Averaging method, Feature point extraction