[1] Shi Z Q, Xu X Y, Ma J J et al. Quantitative detection of cracks in steel using eddy current pulsed thermography[J]. Sensors, 18, 1070(2018).

[2] Li S B, Yang J, Wang Z et al. Review of development and application of defect detection technology[J]. Acta Automatica Sinica, 46, 2319-2336(2020).

[3] Wang Z, Zhang Y W, Yu Y et al. Depth test of pipeline defects by active thermal excitation and infrared thermography[J]. Acta Optica Sinica, 38, 0912003(2018).

[4] Hu J, Xu W P, Gao B et al. Pattern deep region learning for crack detection in thermography diagnosis system[J]. Metals, 8, 612(2018).

[5] Gao B, Li X Q, Woo W L et al. Quantitative validation of eddy current stimulated thermal features on surface crack[J]. NDT & E International, 85, 1-12(2017).

[6] Zhu P P, Tian L L, Cheng Y H. Improvement of defect feature extraction in eddy current pulsed thermography[J]. IEEE Access, 7, 48288-48294(2019).

[7] Huang X G, Yin C, Dadras S et al. Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 1-18(2018).

[8] Zhu P P, Cheng Y H, Banerjee P et al. A novel machine learning model for eddy current testing with uncertainty[J]. NDT&E International, 101, 104-112(2019).

[9] Chen Y M, Sohel F, Ali Shah S A et al. Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography[J]. Optik, 219, 164828(2020).

[10] Liu B L, He J, Yuan X C et al. Intelligent defect identification based on PECT signals and an optimized two-dimensional deep convolutional network[J]. Complexity, 2020, 9518945(2020).

[11] Saeed N, King N, Said Z et al. Automatic defects detection in CFRP thermograms, using convolutional neural networks and transfer learning[J]. Infrared Physics & Technology, 102, 103048(2019).

[12] Gao W, Zhou C, Guo M F. Insulator defect identification via improved YOLOv4 and SR-GAN algorithm[J]. Electric Machines and Control, 25, 93-104(2021).

[13] Li W P, Yang X G, Li C X et al. An improved semi-supervised transfer learning method for infrared object detection neural network[J]. Infrared and Laser Engineering, 50, 243-250(2021).

[14] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 401, 788-791(1999).

[15] Jia X, Sun F M. Vehicle face re-identification algorithm based on Siamese nonnegative matrix factorization[J]. Chinese Journal of Scientific Instrument, 41, 132-139(2020).

[16] Fang W T, Ma P, Cheng Z B et al. 2-dimensional projective non-negative matrix factorization and its application to face recognition[J]. Acta Automatica Sinica, 38, 1503-1512(2012).

[17] Liu S T, Huang D, Wang Y H. Learning spatial fusion for single-shot object detection[EB/OL]. https://arxiv.org/abs/1911.09516

[18] Zhu X K, Lü S C, Wang X et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C], 2778-2788(2021).

[19] Liang X, Li J W, Zhao X L et al. Infrared target imaging liquid level detection method based on deep learning[J]. Acta Optica Sinica, 41, 2110001(2021).

[20] Ju M R, Luo J N, Wang Z B et al. Multi-scale target detection algorithm based on attention mechanism[J]. Acta Optica Sinica, 40, 1315002(2020).

[21] Cheng S, Yang H G, Xu X Q et al. Improved lightweight X-ray aluminum alloy weld defect detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers, 49, 2104005(2022).

[22] He Y, Song K C, Meng Q G et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features[J]. IEEE Transactions on Instrumentation and Measurement, 69, 1493-1504(2020).