[1] 王丽杰. “一长四必”现勘新机制相关问题探究[J]. 法制与经济, 2016( 5): 172- 174.

    Wang LJ. Explore the new mechanism of scene investigation of "one head four must"[J]. Legal and Economy, 2016( 5): 172- 174.

[2] 周佩, 盛善正. 浅谈“一长四必制”对现勘的影响[J]. 产业与科技论坛, 2018, 17(15): 37-38.

    Zhou P, Sheng S Z. The influence of "one head four must" on crime scene investigation[J]. Industrial & Science Tribune, 2018, 17(15): 37-38.

[3] 杨真真, 匡楠, 范露, 等. 基于卷积神经网络的图像分类算法综述[J]. 信号处理, 2018, 34(12): 1474-1489.

    Yang Z Z, Kuang N, Fan L, et al. Review of image classification algorithms based on convolutional neural networks[J]. Journal of Signal Processing, 2018, 34(12): 1474-1489.

[4] Raghavendra U, Fujita H, Bhandary S V, et al. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images[J]. Information Sciences, 2018, 441: 41-49.

[5] Acharya U R, Oh S L, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J]. Computers in Biology and Medicine, 2018, 100: 270-278.

[6] 杨孟京, 唐云祁, 姜晓佳. 基于卷积神经网络的鞋型识别方法研究[J]. 激光与光电子学进展, 2019, 56(19): 191505.

    Yang M J, Tang Y Q, Jiang X J. Novel shoe type recognition method based on convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191505.

[7] 王得成, 陈向宁, 赵峰, 等. 基于卷积神经网络和RGB-D图像的车辆检测算法[J]. 激光与光电子学进展, 2019, 56(18): 181003.

    Wang D C, Chen X N, Zhao F, et al. Vehicle detection algorithm based on convolutional neural network and RGB-D images[J]. Laser & Optoelectronics Progress, 2019, 56(18): 181003.

[8] 李卓容, 王凯旋, 何欣龙, 等. 基于卷积神经网络的足跟着地事件检测算法[J]. 激光与光电子学进展, 2019, 56(21): 211503.

    Li Z R, Wang K X, He X L, et al. Research on heel strike events detection algorithm based on convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211503.

[9] LeCunY, KavukcuogluK, FarabetC. Convolutional networks and applications in vision[C]∥Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May 30-June 2, 2010, Paris, France. New York: IEEE, 2010: 253- 256.

[10] KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]∥Advances in Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, United States. Canada: NIPS, 2012: 1097- 1105.

[11] SzegedyC, LiuW, Jia YQ, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 15523970.

[12] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. ( 2015-04-10)[2019-06-11]. https:∥arxiv.org/abs/1409. 1556.

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

[14] 郝宏奎. 犯罪现勘[M]. 北京: 中国人民公安大学出版社, 2008.

    Hao HK. Crime scene investigation[M]. Beijing: People's Public Security University of China Press, 2008.

[15] 高光斗, 王大中, 朱翔. 中国刑事科学技术大全: 犯罪现勘技术学[M]. 北京: 中国人民公安大学出版社, 2003.

    Gao GD, Wang DZ, ZhuX. Encyclopedia offorensicscience in China: crime scene investigation technology[M]. Beijing: People's Public Security University of China Press, 2003.

[16] NairV, Hinton GE. Rectified linear units improve restricted Boltzmann machines[C]∥Proceedings of the 27th international conference on machine learning (ICML-10), June 21-24, 2010, Haifa, Israel. [S.l.: s.n.], 2010: 807- 814.

[17] GlorotX, BengioY. Understanding the difficulty of training deep feedforward neural networks[C]∥Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, March 2010, Chia Laguna Resort, Sardinia, Italy. [S.l.: s.n.], 2010: 249- 256.

[18] LinM, ChenQ, Yan S C. Network in network[J/OL]. ( 2014-03-04)[2019-06-11]. https:∥arxiv.org/abs/1312. 4400.

[19] IoffeS, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[J/OL]. ( 2015-03-02)[2019-06-11]. https:∥arxiv.org/abs/1502. 03167.

[20] SzegedyC, VanhouckeV, IoffeS, et al. Rethinking the inception architecture for computer vision[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 2818- 2826.

[21] SzegedyC, IoffeS, VanhouckeV, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]∥Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. USA: AAAI, 2017: 4278- 4824.

[22] JiaY, ShelhamerE, DonahueJ, et al. Caffe: convolutional architecture for fast feature embedding[C]∥Proceedings of the 22nd ACM International Conference on Multimedia, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 2014: 675- 678.