[1] Zhao D B, Chen Y R, Lv L. Deep reinforcement learning with visual attention for vehicle classification[J]. IEEE Transactions on Cognitive and Developmental Systems, 2017, 9(4): 356-367.

[2] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

[3] KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]∥Proceedings of the 25th Informational Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada. New York: Curran Associates, 2012: 1097- 1105.

[4] Kang Q, Zhao H D, Yang D X, et al. Lightweight convolutional neural network for vehicle recognition in thermal infrared images[J]. Infrared Physics & Technology, 2020, 104: 103120.

[5] 张洁, 赵红东, 李宇海, 等. 复杂背景下车型识别分类器[J]. 激光与光电子学进展, 2019, 56(4): 041501.

    Zhang J, Zhao H D, Li Y H, et al. Classifier forrecognition of fine-grained vehicle models under complex background[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041501.

[6] 马永杰, 马芸婷, 陈佳辉. 结合卷积神经网络多层特征和支持向量机的车辆识别[J]. 激光与光电子学进展, 2019, 56(14): 141001.

    Ma Y J, Ma Y T, Chen J H. Vehicle recognition based on multi-layer features of convolutional neural network and support vector machine[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141001.

[7] 张苗辉, 张博, 高诚诚. 一种多任务的卷积神经网络目标分类算法[J]. 激光与光电子学进展, 2019, 56(23): 231502.

    Zhang M H, Zhang B, Gao C C. Object classification based on multitask convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231502.

[8] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-10)[2020-09-01]. https:∥arxiv.org/abs/1409. 1556.

[9] SzegedyC, LiuW, Jia YQ, et al.Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA.New York: IEEE Press, 2015.

[10] He KM, Zhang XY, Ren SQ, et al.Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA.New York: IEEE Press, 2016: 770- 778.

[11] 刘航, 汪西莉. 基于注意力机制的遥感图像分割模型[J]. 激光与光电子学进展, 2020, 57(4): 041015.

    Liu H, Wang X L. Remote sensing image segmentation model based on attention mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041015.

[12] 席志红, 袁昆鹏. 基于残差通道注意力和多级特征融合的图像超分辨率重建[J]. 激光与光电子学进展, 2020, 57(4): 041504.

    Xi Z H, Yuan K P. Super-resolution image reconstruction based on residual channel attention and multilevel feature fusion[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041504.

[13] WangF, Jiang MQ, QianC, et al.Residual attention network for image classification[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA.New York: IEEE Press, 2017: 6450- 6458.

[14] Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM:visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 2020, 128(2): 336-359.

[15] KrauseJ, StarkM, JiaD, et al.3D object representations for fine-grained categorization[C]∥2013 IEEE International Conference on Computer Vision Workshops, December 2-8, 2013, Sydney, NSW, Australia.New York: IEEE Press, 2013: 554- 561.

[16] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327.

[17] Zhao B, Wu X, Feng J S, et al. Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia, 2017, 19(6): 1245-1256.

[18] Lin T Y. RoyChowdhury A, Maji S. Bilinear convolutional neural networks for fine-grained visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(6): 1309-1322.

[19] Wang YM, Morariu VI, Davis LS. Learning a discriminative filter bank within a CNN for fine-grained recognition[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA.New York: IEEE Press, 2018: 4148- 4157.