首页|Study Findings from South China University of Technology Provide New Insights into Robotics (Image Restoration Based On Vector Quantization for Robotic Automatic Welding)
Study Findings from South China University of Technology Provide New Insights into Robotics (Image Restoration Based On Vector Quantization for Robotic Automatic Welding)
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NETL
NSTL
Elsevier
New research on Robotics is the subject of a report. According to news reporting originating in Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "For the purpose of robot automatic welding based on a laser stripe welding seam tracking system, a weld noise image restoration algorithm based on vector quantization and synthetic fractal noise is proposed to solve the problems caused by strong noise interference and limited noise dataset in welding seam tracking. The weld noise image restoration is a process of noise loss." Financial support for this research came from National Natural Science Foundation of Guangdong Province. The news reporters obtained a quote from the research from the South China University of Technology, "To achieve this process, a weld noise image restoration learning model based on feature encoding, vector quantization, and feature decoding is constructed, which includes feature encoding to extract image features, vector quantization to produce noise loss, and feature decoding to complete image restoration. To enhance the generalization ability of the weld noise image restoration model and overcome the difficulty of limited data collection in on-site welding, a synthetic welding noise model is proposed based on fractal theory, multidimensional Gaussian distribution, and random region generation algorithm. A large-scale training dataset is generated by randomly initializing parameters, and the potential mapping rela-tionship between the weld noise image and the noise-free image is constructed. Compared with the limited dataset obtained in on-site welding, the synthetic dataset makes the image restoration model more generalizable, and the similarity between the restored welding seam image and the original image reaches 0.85."
GuangzhouPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningRoboticsRobotsVector QuantizationSouth China University of Technology
NETL
NSTL
Elsevier
