[1] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada. New York: IEEE, 770-778(2016).

[2] Seferbekov S, Iglovikov V, Buslaev A et al. Feature pyramid network for multi-class land segmentation. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2018, Salt Lake City, Utah. New York: IEEE, 272-275(2018).

[3] Neubeck A, van Gool L. Efficient non-maximum suppression. [C]∥18th International Conference on Pattern Recognition (ICPR'06), August 20-24, 2006, Hong Kong, China. New York: IEEE, 9210072(2006).

[4] Qi C, Su F. Contrastive-center loss for deep neural networks. [C]∥2017 IEEE International Conference on Image Processing (ICIP), September 17-20, 2017, Beijing, China. New York: IEEE, 2851-2855(2017).

[5] Clevert D A, Unterthiner T, Hochreiter S. Fast. -02-22)[2019-03-02]. https:∥arxiv., org/abs/1511, 07289(2016).

[6] Yang S, Luo P, Loy C C et al. WIDER FACE: a face detection benchmark. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada. New York: IEEE, 5525-5533(2016).

[7] Ou P, Zhang Z, Lu K et al. Object detection in of remote sensing images based on convolutional neural networks[J]. Laser & Optoelectronics Progress, 56, 051002(2019).

[8] LeCun Y, Bottou L, Bengio Y et al. . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/5.726791&rfr_id=trans/tp/2008/08/ttp2008081415.htm

[9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Advances in Neural Information Processing Systems 25 (NIPS 2012), December 3-8, 2012, Harrahs and Harveys, Lake Tahoe. New York: NIPS, 1097-1105(2012).

[10] Long X, Su H S, Liu G H et al. A face recognition algorithm based on angular distance loss function and convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 121505(2018).

[11] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. [C]∥ Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), April 11-13, 2011, Fort Lauderdale, USA. [S.l.: s.n.], 315-323(2011).

[12] Liu F, Liu P Y, Li B et al. Deep learning model design of video target tracking based on TensorFlow platform[J]. Laser & Optoelectronics Progress, 54, 091501(2017).

[13] Ohn-Bar E, Trivedi M M. To boost or not to boost? On the limits of boosted trees for object detection. [C]∥2016 23rd International Conference on Pattern Recognition (ICPR), December 4-8, 2016, Cancun, Mexico. New York: IEEE, 3350-3355(2016).

[14] Yang S, Luo P, Loy C C et al. From facial parts responses to face detection: a deep learning approach. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 3676-3684(2015).

[15] Zhang K P, Zhang Z P, Li Z F et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 23, 1499-1503(2016). http://ieeexplore.ieee.org/document/7553523/

[16] Yang B, Yan J J, Lei Z et al. Aggregate channel features for multi-view face detection. [C]∥IEEE International Joint Conference on Biometrics, September 29-October 2, 2014, Clearwater, FL, USA. New York: IEEE, 14838106(2014).