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