Abstract
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.
Highlights
In recent years, deep learning has made remarkable progress in the three main fields of computer vision image recognition, target detection, and image segmentation
While deep learning approaches are comparable to human vision in many areas, some areas require designing different models for different tasks
A new skeleton extraction method based on deep learning is proposed to extract the laser curve of a subway wheelset
Summary
Deep learning has made remarkable progress in the three main fields of computer vision image recognition, target detection, and image segmentation. Wang Shengchun et al [8] used deep learning E-Net for preprocessing, followed by template matching, and the gray gravity center method for subsequent processing, and Yang Kai et al [9] used UNet network for laser fringe segmentation. The results of these methods often depend on the effect of deep learning preprocessing and consume a great deal of time. The goal of the task of skeleton segmentation is to extract the skeleton with the desired width of one pixel and keep the topology and geometry of the shape consistent with the target extracted from the laser stripe center. The center of the laser fringe can be extracted quickly and accurately by the skeleton extraction network
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