[1] Xingwang Guo. Comparative study on pulsed thermography and modulated thermography of composite honeycomb panels. Acta Materiae Compositae Sinica, 29, 172-179(2012).

[2] [2] Fan Weiming. Research on the technology of GFRPNOMEX honeycomb swich structure defects detection using infrared thermal wave testing method[D]. Harbin: Heilongjiang University of Science Technology, 2022. (in Chinese)

[3] Jiacheng Wei, Junyan Liu, Lin He, et al. Recent progress in infrared thermal imaging nondestructive testing technology. Journal of Harbin University of Science and Technology, 25, 64-72(2020).

[4] Chiwu Bu, Bo Zhao, Tao Liu, et al. Barker coded thermal wave detection and matched filtering for defects in CFRP/Al honeycomb structure. Infrared and Laser Engineering, 50, 20210050(2021).

[5] Yanhong Li, Wanping Jin, Danggang Yang, et al. Thermal wave nondestructive testing of honeycomb structure. Infrared and Laser Engineering, 45-48(200635).

[6] Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 521, 436-444(2015).

[7] S J Pan, Q Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[8] [8] Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural wks [C]NIPS, 2014, 2: 33203328.

[9] [9] Simonyan K, Zisserman A. Very deep convolutional wks f largescale image recognition [C] ICLR, 2015: 114.

[10] Jinghui Xu, Mingye Shao, Yichen Wang, et al. Recognition of corn leaf spot and rust based on transfer learning with convolutional neural network. Transactions of the Chinese Society for Agricultural Machinery, 51, 230-236, 253(2020).

[11] [11] He K, Zhang X, Ren S, et al. Deep residual learning f image recognition [C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2016: 770778.

[12] Xiao Feng, Dandan Li, Wenjun Wang, et al. Image recognition of wheat leaf diseases based on lightweight convolutional neural network and transfer learning. Journal of Henan Agricultural Sciences, 50, 174-180(2021).

[13] [13] Sler M, Howard A, Zhu M, et al. Mobilev2: Inverted residuals linear bottlenecks[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2018: 45104520.

[14] [14] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture f computer vision [C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2016: 28182826.

[15] Peng Cai, Lei Yang, Junli Luo. Fabric defect detection method based on fusion of convolutional neural network models. Journal of Beijing Institute of Fashion Technology (Natural Science Edition), 40, 55-62(2020).