[5] LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition［J］.Proceedings of the IEEE, 1998, 86(11):2278-2324.

[6] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas, NV:IEEE, 2016:770-778.

[7] SZEGEDY C, LIU W, JIA Y Q, et al.Going deeper with convolutions［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Boston, MA:IEEE, 2015:1-9.

[8] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas, NV:IEEE, 2016:2818-2826.

[9] SZEGEDY C, IOFFE S, VANHOUCKE V, et al.Inception-v4, inception-ResNet and the impact of residual connections on learning［EB/OL］.(2018-08-23)［2021-08-20］.https://arxiv.org/abs/1602.07261.

[10] WOO S H, PARK J C, LEE J Y, et al.CBAM:convolutional block attention module［EB/OL］.(2018-07-18)［2021-08-20］.https://arxiv.org/abs/1807.06521.

[11] WANG Q L, WU B G, ZHU P F, et al.ECA-Net:efficient channel attention for deep convolutional neural networks［C］//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle, WA:IEEE, 2020:11531-11539.

[12] HU J, SHEN L, ALBANIE S.Squeeze-and-excitation networks［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8):2011-2023.

[13] ZHAO M H, ZHONG S S, FU X Y, et al.Deep residual shrinkage networks for fault diagnosis［J］.IEEE Transactions on Industrial Informatics, 2019, 16(7):4681-4690.

[15] KEYDEL E R, LEE S W, MOORE J.MSTAR extended operating conditions:a tutorial ［C］//Proceeding Volume Algorithms for Synthetic Aperture Radar Imagery Ⅲ.Orlando, FL:SPIE, 1996:228-242.