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Model optimization and acceleration method based on meta-learning and model pruning for laser vision weld tracking system

Yanbiao Zou (School of Mechanical and Automative Engineering, South China University of Technology, Guangzhou, China)
Jianhui Yang (School of Mechanical and Automative Engineering, South China University of Technology, Guangzhou, China)

Industrial Robot

ISSN: 0143-991X

Article publication date: 17 September 2024

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Abstract

Purpose

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

Design/methodology/approach

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Findings

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

Originality/value

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Keywords

Acknowledgements

This work is supported by the Natural Science Foundation of Guangdong Province (Grant No. 2021A1515011736 and No. 2023A1515012938). The authors gratefully acknowledge these support agencies.

Disclosures: The authors declare no conflicts of interest.

Citation

Zou, Y. and Yang, J. (2024), "Model optimization and acceleration method based on meta-learning and model pruning for laser vision weld tracking system", Industrial Robot, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IR-05-2024-0233

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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