Image restoration based on vector quantization for robotic automatic welding

Abstract

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. 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 relationship 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. In the welding seam tracking experiment, for multiple sets of different data, the average tracking accuracy of the welding seam tracking algorithm based on image restoration is 0.2 mm, which verifies that the proposed image restoration algorithm can improve the robustness of automatic welding.