2021
DOI: 10.1109/access.2021.3098833
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The Centerline Extraction Algorithm of Weld Line Structured Light Stripe Based on Pyramid Scene Parsing Network

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 pixel… Show more

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Cited by 16 publications
(3 citation statements)
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“…Applying deep learning technology can effectively extract data features and improve classification accuracy [33]. With the development of deep learning, many neural network algorithms for semantic segmentation have been proposed, such as fully convolutional networks (FCNs) [34], pyramid scene parsing network (PSPNets) [35], SegNet [36], etc. Many scholars have introduced these algorithms into the meteorological field [37], such as the deep semantic segmentation model that extracts multi-source observation data from satellites, radar, and lightning detectors introduced by Zhou Kanghui [38].…”
Section: Introductionmentioning
confidence: 99%
“…Applying deep learning technology can effectively extract data features and improve classification accuracy [33]. With the development of deep learning, many neural network algorithms for semantic segmentation have been proposed, such as fully convolutional networks (FCNs) [34], pyramid scene parsing network (PSPNets) [35], SegNet [36], etc. Many scholars have introduced these algorithms into the meteorological field [37], such as the deep semantic segmentation model that extracts multi-source observation data from satellites, radar, and lightning detectors introduced by Zhou Kanghui [38].…”
Section: Introductionmentioning
confidence: 99%
“…Scholars have made exploration and research in relevant aspects. Representative works include Alwaheba et al [4,5] applied scanning contact potentiometry for defects detection, and for determining the location coordinates of defects in welded joints, Shen et al [6] proposed water flooding segmentation algorithm to weld defect detection, Chen et al [7] extracted X-ray weld image defects based on SUSAN algorithm, Li et al [8] identified weld defects based on independent component analysis, Yu et al [9,10] extracted weld centerline based on pyramid sparse network, Abdelkader et al [11] considered the characteristics of low contrast, poor quality, and uneven illumination of X-ray images, and studied weld defect extraction based on X-ray images. Ding et al [12] proposed the wavelet soft and hard threshold compromise denoising method, Patil et al [13] proposed the techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features, Boaretto et al [14] extracted potential defects based on feedforward multilayer perceptron with back propagation learning algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, using deep learning techniques to extend the performance of traditional algorithms is becoming popular among researchers. Liu et al [2], Zhao et al [3], and Yu et al [4] proposed neural networks for the noise reduction process before centerline extraction, respectively. Learning-based methods perform automatic laser stripe region detection and segmentation by learning the distribution properties of the noise from a large data set, which has improved the performance of subsequent algorithms for extracting the centerline.…”
Section: Introductionmentioning
confidence: 99%