[1] LiuW, AnguelovD, ErhanD, et al.SSD: single shot MultiBox detector[M] ∥Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 21- 37.

[2] 张红颖, 王赛男, 胡文博. 改进的基于卷积神经网络的人数估计方法[J]. 激光与光电子学进展, 2018, 55(12): 121503.

    Zhang H Y, Wang S N, Hu W B. Improved method for estimating number of people based on convolution neural network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121503.

[3] GirshickR, DonahueJ, DrrellT, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of 2014 IEEE International Conference Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA.New York: IEEE Press, 2014: 580- 587.

[4] GirshickR. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile.New York: IEEE Press, 2015: 1440- 1448.

[5] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[6] Dai JF, LiY, He KM, et al. ( 2016-05-20)[2020-08-15]. https:∥arxiv.org/abs/1605. 06409.

[7] Lin TY, DollárP, GirshickR, et al.Feature pyramid networks for object detection[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA.New York: IEEE Press, 2017: 936- 944.

[8] RedmonJ, DivvalaS, GirshickR, et al.You only look once: unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE Press, 2016: 779- 788.

[9] JeongJ, ParkH, Kwak N. Enhancement of SSD by concatenating feature maps for object detection[EB/OL]. ( 2017-05-26)[2020-08-15]. https:∥arxiv.org/abs/1705. 09587.

[10] Fu CY, LiuW, RangaA, et al. ( 2017-01-23)[2020-08-15]. https:∥arxiv.org/abs/1701. 06659.

[11] Leng J X, Liu Y. An enhanced SSD with feature fusion and visual reasoning for object detection[J]. Neural Computing and Applications, 2019, 31(10): 6549-6558.

[12] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-04-10)[2018-11-25]. https:∥arxiv.org/abs/1409.1556v6.

[13] Jia YQ, ShelhamerE, DonahueJ, et al.Caffe: convolutional architecture for fast feature embedding[C]∥Proceedings of the ACM International Conference on Multimedia-MM'14, November 3-7, 2014. Orlando, Florida, USA. New York: ACM Press, 2014: 675- 678.

[14] 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 Press, 2016: 770- 778.

[15] Cao G M, Xie X M, Yang W Z, et al. Feature-fused SSD: fast detection for small objects[J]. Proceedings of SPIE, 2018, 10615: 106151E.

[16] 赵亚男, 吴黎明, 陈琦. 基于多尺度融合SSD的小目标检测算法[J]. 计算机工程, 2020, 46(1): 247-254.

    Zhao Y N, Wu L M, Chen Q. Small object detection algorithm based on multi-scale fusion SSD[J]. Computer Engineering, 2020, 46(1): 247-254.

[17] ErhanD, SzegedyC, ToshevA, et al.Scalable object detection using deep neural networks[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA.New York: IEEE Press, 2014: 2155- 2162.

[18] SzegedyC, ReedS, ErhanD, et al., high-quality objectdetection[EB/OL]. ( 2014-12-03)[2020-08-15]. https:∥arxiv.org/abs/1412. 1441.

[19] 吉祥凌, 吴军, 易见兵, 等. 基于深度学习的管制物品自动检测算法研究[J]. 激光与光电子学进展, 2019, 56(18): 180402.

    Ji X L, Wu J, Yi J B, et al. Automatic detection algorithm for controlled items based on deep learning[J]. Laser & Optoelectronics Progress, 2019, 56(18): 180402.