Abstract
Based on the good feature learning ability of the pyramid scene parsing network, a method for extracting the centerline of structured light stripes of weld lines based on the pyramid scene parsing network and Steger algorithm is proposed. This method avoids the traditional complex weld image preprocessing technology, and simplifies the operation steps of extracting the centerline of the structured light stripe of the weld image line. In this paper, the pyramid scene parsing network is used to predict the pixels containing weld feature information. Through the pyramid pooling module, the local and global context feature information is fused to supplement the feature information of the weld edge, and then the Steger algorithm is used to extract the weld feature centerline. The results show that the method in this paper can accurately extract the centerline position of the structured light stripe of the weld line under the interference of reflection, and the average value reaches 86.8% on the accuracy evaluation index mean intersection over union, the 18.93 pixels on the weld extraction accuracy index root mean square error, and the average time of extracting the center line of structured light stripe of weld line is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.188s$ </tex-math></inline-formula> .
Highlights
In recent years, with the rapid development of automation process in many industries such as industrial manufacturing, and the rapid improvement of related technologies, relying on robots for metal welding operation has gradually become an indispensable link in the production process
This method effectively removes the influence of background noise, but it takes too long and affects efficiency.these methods for processing weld images need to cooperate with complex image preprocessing techniques due to the smoke and dust conditions and light
In this paper, a central line extraction algorithm of a weld line structured light stripe based on PSPNet is proposed
Summary
With the rapid development of automation process in many industries such as industrial manufacturing, and the rapid improvement of related technologies, relying on robots for metal welding operation has gradually become an indispensable link in the production process. Jie Han [6] proposed a variable threshold segmentation algorithm based on Otsu threshold (Otus) This method effectively removes the influence of background noise, but it takes too long and affects efficiency.these methods for processing weld images need to cooperate with complex image preprocessing techniques due to the smoke and dust conditions and light. A weld laser line feature extraction method based on PSPnet and Steger [12] algorithm is proposed This method can accurately obtain the position information of the weld center line in the weld image, which can ensure the extraction accuracy of the weld center line and greatly simplify the operation steps. In the formula, Wt is the weight matrix of the tth iteration, Vt is the weight update value of the tth iteration, α is the basic learning rate of the negative gradient, μ is the weight of the weight update value Vt, used to weight the influence of the previous gradient direction on the current gradient descent direction
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