Laser & Optoelectronics Progress, Volume. 56, Issue 22, 221003(2019)

Light-Weight Multi-Object Detection Network Based on Inverted Residual Structure

Wanjun Liu, Mingyue Gao, Haicheng Qu*, and Lamei Liu
Author Affiliations
  • College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • show less
    References(24)

    [4] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 580-587(2014).

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

    [7] 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, 39, 1137-1149(2017).

    [8] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks. [C]∥Advances in Neural Information Processing Systems 29 (NIPS 2016), December 5-10, 2016, Centre Convencions Internacional Barcelona, Barcelona SPAIN. Canada: NIPS, 379-387(2016).

    [12] Redmon J, Divvala S, Girshick R 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, 779-788(2016).

    [13] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6517-6525(2017).

    [14] Redmon J. -04-08)[2019-03-01]. https:∥arxiv., org/abs/1804, 02767(2018).

    [15] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. European conference on computer vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9905, 21-37(2016).

    [16] Iandola F N, Han S, Moskewicz M W et al. -11-04)[2019-03-01]. https:∥arxiv., org/abs/1602, 07360(2016).

    [17] Howard A G, Zhu M L, Chen B et al. -04-17)[2019-03-01]. https:∥arxiv., org/abs/1704, 04861(2017).

    [18] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4510-4520(2018).

    [19] Yoon J, Hwang S J. Combined group and exclusive sparsity for deep neural networks. [C]∥Proceedings of the 34th International Conference on Machine Learning, August 6-11, 2017, Sydney, NSW, Australia. New York: ACM, 70, 3958-3966(2017).

    [20] Liu Z, Li J G, Shen Z Q et al. Learning efficient convolutional networks through network slimming. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2736-2744(2017).

    [21] Zhou S C, Wu Y X, Ni Z K et al. -02-02)[2019-03-01]. https:∥arxiv., org/abs/1606, 06160(2018).

    [22] Sainath T N, Kingsbury B, Sindhwani V et al. Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets. [C]∥2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 26-31, 2013, Vancouver, BC, Canada. New York: IEEE, 6655-6659(2013).

    [23] Hinton G, Vinyals O. -03-09)[ 2019-03-01]. https:∥arxiv., org/abs/1503, 02531(2015).

    Tools

    Get Citation

    Copy Citation Text

    Wanjun Liu, Mingyue Gao, Haicheng Qu, Lamei Liu. Light-Weight Multi-Object Detection Network Based on Inverted Residual Structure[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221003

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Mar. 27, 2019

    Accepted: May. 17, 2019

    Published Online: Nov. 2, 2019

    The Author Email: Qu Haicheng (quhaicheng@lntu.edu.cn)

    DOI:10.3788/LOP56.221003

    Topics