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

Sensors

Lijuan Yang¹  Tianci Jiang²  Chunjiang Li³  Xiaorong Gao⁴  Kai Yang⁴  Yong Wang⁴  Shuai Luo⁴ 

[1] School of Mathematics, Sichuan Normal University, Chengdu 610066, China;School of Mechanical Engineering, Waseda University, Kitakyushu 8080135, Japan;School of Mechanics, Zhejiang University, Hangzhou 310058, China;School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;

关键词: deep learning;    semantic segmentation;    laser curve extraction;    image processing;   

DOI  :  10.3390/s22030859

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown