Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis rep...

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Vydané v:Sensors Ročník 22; číslo 3; s. 859
Hlavní autori: Luo, Shuai, Yang, Kai, Yang, Lijuan, Wang, Yong, Gao, Xiaorong, Jiang, Tianci, Li, Chunjiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 23.01.2022
MDPI
Predmet:
Accuracy
Algorithms
Analysis
Chemical technology
Datasets
Deep Learning
deep learning; semantic segmentation; laser curve extraction; image processing
image processing
Image Processing, Computer-Assisted
laser curve extraction
Lasers
Methods
Neural networks
Noise
Normal distribution
semantic segmentation
Skeleton
TP1-1185
China
image processing
deep learning
laser curve extraction
semantic segmentation
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22030859