融合卷积神经网络和双边滤波的相贯线焊缝提取算法

doi:10.13229/j.cnki.jdxbgxb.20230197

Abstract

Abstract:

The extraction accuracy of intersecting line weld affects the accuracy of industrial welding， which is an important step of weld technology and one of the key issues in industrial research. In order to solve the problems existing in the traditional method， the intersection line weld extraction algorithm based on convolution neural network and bilateral filtering is proposed. Firstly， the weld image of the intersecting line is grayed and enhanced by the contour wave transformation method； Secondly， the weld image is de-noised by bilateral filtering method； Finally， the intersecting line weld seam is extracted through the full convolution neural network. The experimental results show that the proposed method has higher accuracy， faster speed and better overall application effect.

Key words: Image denoising, convolution neural network, Image enhancement processing, Bilateral filtering, extraction of intersecting line weld

CLC Number:

TG409

Jin-zhou ZHANG,Shi-qing JI,Chuang TAN. Fusion algorithm of convolution neural network and bilateral filtering for seam extraction[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2313-2318.

Figures/Tables 4

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序号	偏差［x/y pixel］
序号	文献［3］算法	文献［4］算法	所提算法
1	［5，6］	［5，10］	［2，3］
2	［7，8］	［4，8］	［1，3］
3	［6，4］	［2，9］	［2，2］
4	［5，7］	［3，8］	［2，1］
5	［8，6］	［4，7］	［3，1］

Fusion algorithm of convolution neural network and bilateral filtering for seam extraction

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