[1] [1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks［C］//Advances in neural information processing systems. Lake Tahoe, Nevada, USA: Curran Associates Inc, 2012: 1097-1105.

[2] [2] FENG W, WANG Y D, ZHANG L. Weighted joint dimensionality extraction and classification recognition algorithm ［J］. Laser Technology, 2018, 42(5): 666-672(in Chinese).

[3] [3] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: Common objects in context［C］//European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 740-755.

[4] [4] EVERINGHAM M, Van GOOL L, WILLIAMS C K I, et al. The pascal visual object classes (voc) challenge［J］. International Journal of Computer Vision, 2010, 88(2): 303-338.

[5] [5] RUSSAKOVSKY O, DENG J, SU H, et al. Imagenet large scale visual recognition challenge［J］. International Journal of Computer Vision, 2015, 115(3): 211-252.

[6] [6] BENENSON R, OMRAN M, HOSANG J, et al. Ten years of pedestrian detection, what have we learned［C］//European Conference on Computer Vision. Zurich, Switzerland : Springer, 2014: 613-627.

[7] [7] ZHANG S, BENENSON R, OMRAN M, et al. How far are we from solving pedestrian detection［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 1259-1267.

[8] [8] COSTEA A D, NEDEVSCHI S. Semantic channels for fast pedestrian detection［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE,2016: 2360-2368.

[9] [9] HOSANG J, OMRAN M, BENENSON R, et al. Taking a deeper look at pedestrians［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2015: 4073-4082.

[10] [10] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2014: 580-587.

[11] [11] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.

[12] [12] ZHANG L, LIN L, LIANG X, et al. Is faster R-CNN doing well for pedestrian detection［C］//European Conference on computer Vision. Amsterdam, Netherlands: Springer, 2016: 443-457.

[13] [13] ZHANG Sh Sh, BENENSON R, SCHIELE B. Citypersons: A diverse dataset for pedestrian detection［C］//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA: IEEE, 2017: 3.

[14] [14] LI J, LIANG X, SHEN S M, et al. Scale-aware fast R-CNN for pedestrian detection［J］. IEEE Transactions on Multimedia, 2018, 20(4): 985-996.

[15] [15] GIRSHICK R. Fast R-CNN［C］//Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2015: 1440-1448.

[16] [16] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-V4, inception-resnet and the impact of residual connections on learning［J］.Computer Vison and Pattern Recognition, 2016, 23(2):07261.

[17] [17] DOLLR P, WOJEK C, SCHIELE B, et al. Pedestrian detection: A benchmark［C］//IEEE Conference on Computer Vision and Pattern Recognition, 2009. New York, USA: IEEE, 2009: 304-311.

[18] [18] GUADARRAMA S. Tensorflow-slim image classification model library［OL］.(2018-07-05)［2018-12-31］.https://github.com/tensorflow/models/tree/master/research/slim.

[19] [19] UIJLINGS J R R, van de SANDE K E A, GEVERS T, et al. Selective search for object recognition［J］. International Journal of Computer Vision, 2013, 104(2): 154-171.

[20] [20] ZITNICK C L, DOLLR P. Edge boxes: Locating object proposals from edges［C］//European Conference on Computer Vision. Zurich, Switzerland : Springer, 2014: 391-405.

[21] [21] KONG T, YAO A, CHEN Y, et al. Hypernet: Towards accurate region proposal generation and joint object detection［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 845-853.