CycleGAN-Based Data Augmentation for Subgrade Disease Detection in GPR Images with YOLOv5

被引:4
作者
Yang, Yang [1 ]
Huang, Limin [1 ]
Zhang, Zhihou [1 ]
Zhang, Jian [1 ]
Zhao, Guangmao [2 ]
机构
[1] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 611756, Peoples R China
[2] China Railway Design Corp, Tianjin 300142, Peoples R China
关键词
disease detection; ground-penetrating radar; YOLO; GAN; semi-supervised learning; DEEP NEURAL-NETWORKS; CLASSIFICATION; RECOGNITION;
D O I
10.3390/electronics13050830
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vehicle-mounted ground-penetrating radar (GPR) technology is an effective means of detecting railway subgrade diseases. However, existing methods of GPR data interpretation largely rely on manual identification, which is not only inefficient but also highly subjective. This paper proposes a semi-supervised deep learning method to identify railway subgrade diseases. This method addresses the sample imbalance problem in the defect dataset by utilizing a data augmentation method based on a generative adversarial network model. An initial network model for disease identification is obtained by training the YOLOv5 network with a small number of existing samples. The intelligently extended samples are then labeled to achieve a balance in the disease samples. The network is trained to improve the recognition accuracy of the intelligent model using a more complete dataset. The experimental results show that the accuracy of the proposed method can reach up to 94.53%, which is 23.85% higher than that of the supervised learning model without an extended dataset. This has strong industrial application value for railway subgrade disease detection as the potential learning ability of the model can be explored to a greater extent, thereby improving the recognition accuracy of subgrade diseases.
引用
收藏
页数:18
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