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Welding splash and arc noise reduction imaging model based on computationally efficient pairwise response serving welding process library

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Abstract

In the process of vision-assisted robot automatic welding, splash and arc noise will interfere with the useful information in the image, which has a serious impact on the subsequent vision-based weld seam recognition and tracking algorithm. Although various methods have been proposed to achieve noise reduction, those are implemented from a perspective of image processing or intelligent algorithm. In this paper, we propose a pairwise response noise reduction model (PRNM) from the imaging perspective of seam tracking camera, which provides an image processing-free method to suppress welding splash and arc. Inverse response law and the loss function is established and solved by using singular value decomposition, and then the irradiance of reference scene is restored. A tone mapping approach is proposed based on the advantages of full-range compression and joint estimator, followed by mapping the obtained irradiance distribution to a high dynamic range (HDR) image. The grayscale correspondences of two reference images and the HDR image are reflected in the form of a two-dimensional lookup table (LUT). For welding noise characteristics, an alignment strategy is proposed to further correct the LUT to form a PRNM, which responds to all pairwise inputs with high computational efficiency. A batch of PRNMs have potential to form imaging solutions serving a welding process library. Experimental results reveal that the proposed model has a good effect in noise reduction, and the real-time performance is suitable for weld seam tracking.

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Acknowledgements

This work was supported in part by the Key Research and Development Program of Guangdong Province 2020 B090928002, National Natural Science Foundation of China (62176072) and Self-Planned Task NO.SKLRS20 2111B of State Key Laboratory of Robotics and System (HIT).

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Correspondence to Ruifeng Li.

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Qin, Z., Wang, K. & Li, R. Welding splash and arc noise reduction imaging model based on computationally efficient pairwise response serving welding process library. Machine Vision and Applications 33, 93 (2022). https://doi.org/10.1007/s00138-022-01342-6

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