• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0415009 (2021)
Yuxin Li, Fan Yang*, Zhao Liu, and Yazhong Si
Author Affiliations
  • School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
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    DOI: 10.3788/LOP202158.0415009 Cite this Article Set citation alerts
    Yuxin Li, Fan Yang, Zhao Liu, Yazhong Si. Classification Method of Crossing Vehicle Based on Improved Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415009 Copy Citation Text show less
    References

    [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, 9, 356-367(2017). http://ieeexplore.ieee.org/document/7580631/

    [2] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1023/B:VISI.0000029664.99615.94&rfr_id=trans/tp/2008/10/ttp2008101683.htm

    [3] Krizhevsky A, Sutskever I, Hinton G E. 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, 1097-1105(2012).

    [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, 104, 103120(2020).

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

    [6] 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, 56, 141001(2019).

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

    [8] Simonyan K. -04-10)[2020-09-01]. https:∥arxiv., org/abs/1409, 1556(2015).

    [9] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA.(2015).

    [10] He K M, Zhang X Y, Ren S Q 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., 770-778(2016).

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

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

    [13] Wang F, Jiang M Q, Qian C 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., 6450-6458(2017).

    [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, 128, 336-359(2020).

    [15] Krause J, Stark M, Jia D 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., 554-561(2013).

    [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, 42, 318-327(2020).

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

    [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, 40, 1309-1322(2018).

    [19] Wang Y M, Morariu V I, Davis L S. 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., 4148-4157(2018).

    Yuxin Li, Fan Yang, Zhao Liu, Yazhong Si. Classification Method of Crossing Vehicle Based on Improved Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415009
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