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基于深度学习骨架提取网络的轮对激光曲线提取。

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

机构信息

School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.

School of Mathematics, Sichuan Normal University, Chengdu 610066, China.

出版信息

Sensors (Basel). 2022 Jan 23;22(3):859. doi: 10.3390/s22030859.

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.

摘要

本文提出了一种新的激光条纹中心提取算法。基于深度学习骨架提取网络,可以快速准确地提取激光条纹中心。骨架提取是将形状图像简化为其近似中轴线表示的过程,同时保持图像的拓扑和几何形状。骨架提取是拓扑和几何形状分析的重要步骤。根据轮对激光曲线数据集的特点,提出了一种新的骨架提取网络,即分层骨架网络(LuoNet)。所提出的架构具有三个级别的编码器-解码器网络,并且在每个级别的编码器和解码器网络之间设计了 YE 模块连接。在轮对激光曲线数据集中,F1_score 可以达到 0.714。与传统的激光曲线中心提取算法相比,所提出的 LuoNet 算法具有运行时间短、精度高和提取结果稳定的优点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5c3/8838502/407a5a1a45ee/sensors-22-00859-g001.jpg

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