Author(s)

Xiran Su ¹

Affiliation(s)

¹ Beijing Sineva Robot Technology Co., Ltd, Beijing, 100176, China

Corresponding Author

Xiran Su

ABSTRACT

Visual recognition and positioning optimization of industrial robots play a vital role in automatic production. Aiming at this problem, this study proposes a method of visual recognition and positioning optimization based on deep learning, namely, Multi-Scale Attention-based Deep Learning Visual Localization Network (MSA-DLVN). By introducing a multi-scale attention mechanism, this method can effectively improve the visual perception and positioning accuracy of industrial robots in complex environments. The comparative experiments on real scene data sets show that MSA-DLVN method is significantly superior to traditional methods in visual positioning optimization and workpiece recognition. Specifically, the positioning accuracy of MSA-DLVN method is 1.3cm higher than that of baseline method, and the accuracy of workpiece identification is 9 percentage points higher. In addition, MSA-DLVN method maintains good robustness and universality in different experimental scenarios and data sets. This study provides a reliable solution for industrial robot visual recognition and positioning optimization, which is helpful to promote the development of industrial automation production.

KEYWORDS

Deep learning; Industrial robots; Visual recognition and positioning; MSA-DLVN

CITE THIS PAPER

Xiran Su, Vision Recognition and Positioning Optimization of Industrial Robots Based on Deep Learning. Journal of Artificial Intelligence Practice (2024) Vol. 7: 49-55. DOI: http://dx.doi.org/10.23977/jaip.2024.070207.

REFERENCES

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