Eddy current thermal imaging detection technology based on convolutional neural network

doi:10.3969/j.issn.1000-1565.2025.06.011

Abstract

Abstract: For eddy current excitation thermal imaging detection, the faster region convolutional neural network(Faster R-CNN)algorithm is improved, and the Inception-V4 structure is introduced as a feature extraction network to improve the accuracy and speed of defect recognition. Experimental methods are used to obtain different types of defect data, and convolutional neural networks are imported for training and testing, and the results are compared with the results processed by common VGG and ResNet networks. The results show that the model proposed in this paper can meet the requirements of defect recognition, and is better than the traditional algorithm in terms of recognition accuracy and recognition speed.

Key words: non-destructive testing, equipment maintenance, fault diagnosis, defect identification

CLC Number:

TG115

LI Jianyu, CHENG Jiaojiao, ZHU Heming, ZHENG Wenpei. Eddy current thermal imaging detection technology based on convolutional neural network[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(6): 653-660.

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