[1] Yang L J, Xing Y H, Zhang J et al. Crack defect detection of aluminum plate based on electromagnetic ultrasonic guided wave[J]. Chinese Journal of Scientific Instrument, 39, 150-160(2018).

[2] Wu X J, Zhang Q, Shen G T. Review on advances in pulsed eddy current nondestructive testingtechnology[J]. Chinese Journal of Scientific Instrument, 37, 1698-1712(2016).

[3] Cheng J, Yang J Q, Qiu J H et al. Visualization of meso-structure of carbon fiber reinforced polymer based on eddy current imaging[J]. Acta Materiae Compositae Sinica, 35, 2074-2083(2018).

[4] Zhou D Q, Zuo X F, You L H et al. Design of flaw-detecting system for ferromagnetic material based on pulsed eddy current[J]. Transducer and Microsystem Technologies, 31, 121-124(2012).

[5] Cheng L, Tian G Y. Surface crack detection for carbon fiber reinforced plastic (CFRP) materials using pulsed eddy current thermography[J]. IEEE Sensors Journal, 11, 3261-3268(2011). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5772899

[6] Yuan X C, Wu L S, Chen H W. Rail image segmentation based on Otsu threshold method[J]. Optics and Precision Engineering, 24, 1772-1781(2016).

[7] Zhang Y, Wang F L. Image segmentation for watershed algorithm based on wavelet transform[J]. Journal of Chinese Computer Systems, 35, 1382-1386(2014).

[8] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

[9] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science., 9351, 234-241(2015).

[10] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C]. //Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec. New York: Curran Associates, 2, 2672-2680(2014).

[11] Li L F, Hu M. Method forsmall-bridge-crack segmentation based on generative adversarial network[J]. Laser & Optoelectronics Progress, 56, 101004(2019).

[12] Ai L M, Shi K Z. Low-grade gliomas MR images segmentation based on conditional generative adversarial networks[J]. Laser & Optoelectronics Progress, 57, 221004(2020).

[13] Teng W X, Wang N, Chen T S et al. Deep adversarial domain adaptation method for cross-domain classification in high-resolution remote sensing images[J]. Laser & Optoelectronics Progress, 56, 112801(2019).

[14] Du Z X, Yin J Y, Yang J. Remote sensing aircraft image detection based on semi-supervised learning[J]. Laser & Optoelectronics Progress, 57, 061009(2020).

[15] Luc P, Couprie C, Chintala S et al. Semantic segmentation using adversarial networks[EB/OL]. (2016-11-25)[2020-08-17]. https://arxiv.org/abs/1611.08408

[16] Xue Y, Xu T, Zhang H et al. SegAN: adversarial network with multi-scale L1 loss for medical image segmentation[J]. Neuroinformatics, 16, 383-392(2018). http://europepmc.org/abstract/MED/29725916

[17] Yang S, Chen L F, Shi Y et al. Semantic segmentation of blue-green algae based on deep generative adversarial net[J]. Journal of Computer Applications, 38, 1554-1561(2018).