留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于LGC的反残差目标检测算法

张云佐 李文博 郑婷婷

张云佐,李文博,郑婷婷. 基于LGC的反残差目标检测算法[J]. 北京航空航天大学学报,2023,49(6):1287-1293 doi: 10.13700/j.bh.1001-5965.2021.0452
引用本文: 张云佐,李文博,郑婷婷. 基于LGC的反残差目标检测算法[J]. 北京航空航天大学学报,2023,49(6):1287-1293 doi: 10.13700/j.bh.1001-5965.2021.0452
ZHANG Y Z,LI W B,ZHENG T T. Inverted residual target detection algorithm based on LGC[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1287-1293 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0452
Citation: ZHANG Y Z,LI W B,ZHENG T T. Inverted residual target detection algorithm based on LGC[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1287-1293 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0452

基于LGC的反残差目标检测算法

doi: 10.13700/j.bh.1001-5965.2021.0452
基金项目: 广东省重点领域研发计划(2019B010137006);国家自然科学基金(61702347,62027801,61972267);河北省自然科学基金(F2017210161)
详细信息
    通讯作者:

    E-mail:zhangyunzuo888@sina.com

  • 中图分类号: TP391

Inverted residual target detection algorithm based on LGC

Funds: Key-Area Research and Development Program of Guangdong Province (2019B010137006); National Natural Science Foundation of China (61702347,62027801,61972267); Natural Science Foundation of Hebei Province of China (F2017210161)
More Information
  • 摘要:

    基于深度学习的目标检测是计算机视觉领域的研究热点,目前主流的目标检测模型大多通过增加网络深度和宽度以获得更好的检测效果,但容易导致参数量增加、检测速度降低的问题。为兼顾检测精度与速度,借鉴Ghost卷积和分组卷积的轻量化思想,提出了一种高效的轻量级Ghost卷积(LGC)模型,以采用更少的参数获得更多的特征图。在该卷积模型的基础上引入反残差结构重新设计了CSPDarkNet53,生成了一种基于LGC的反残差特征提取网络,以提高网络对全局特征信息的提取能力。使用反残差特征提取网络替换YOLOv4的骨干网络,辅以深度可分离卷积进一步减少参数,提出了一种反残差目标检测算法,以提升目标检测的整体性能。实验结果表明:相比于主流的目标检测算法,所提算法在检测精度相当的前提下,模型参数量和检测速度具有明显的优势。

     

  • 图 1  Ghost卷积结构

    Figure 1.  Ghost convolution structure

    图 2  LGC结构

    Figure 2.  LGC structure

    图 3  残差单元网络结构

    Figure 3.  Residual cell network structure

    图 4  LGC-YOLOv4网络结构

    Figure 4.  LGC-YOLOv4 network structure

    表  1  LGC-IRNet网络结构

    Table  1.   LGC-IRNet network structure

    网络层次通道数尺寸/步长输出
    Conv2D323×3/1416×416×32
    Conv2D643×3/2208×208×64
    LGC32
    LGC64
    残差块208×208×64
    Conv2D1283×3/2104×104×128
    LGC64
    LGC128
    残差块104×104×128
    Conv2D2563×3/252×52×256
    LGC128
    LGC256
    残差块52×52×256
    Conv2D5123×3/226×26×512
    LGC256
    LGC512
    残差块26×26×512
    Conv2D10243×3/213×13×1024
    LGC512
    LGC1024
    残差块13×13×1024
    下载: 导出CSV

    表  2  不同算法性能比较

    Table  2.   Performance comparison of different algorithms

    算法模型体积/MB平均精度/%检测速度/
    (帧·s−1
    YOLOv4[3]24683.3266
    Ghost-YOLOv4[9]18680.0869
    GhostNet-YOLOv4[23]4378.7383
    LGC-YOLOv4-PANet14985.0673
    LGC-YOLOv45383.1782
    下载: 导出CSV

    表  3  不同骨干网络的网络模型性能比较

    Table  3.   Performance comparison of different backbone network models

    主干网络平均精度/%模型体积/MB
    Conv2DGhostLGCConv2DGhostLGC
    CSPDarkNet5383.3280.0880.68246186152
    GhostNet78.7378.184339
    IRNet84.0885.06172149
    下载: 导出CSV
  • [1] 赵永强, 饶元, 董世鹏, 等. 深度学习目标检测方法综述[J]. 中国图象图形学报, 2020, 25(4): 629-654. doi: 10.11834/jig.190307

    ZHAO Y Q, RAO Y, DONG S P, et al. Survey on deep learning object detection[J]. Journal of Image and Graphics, 2020, 25(4): 629-654(in Chinese). doi: 10.11834/jig.190307
    [2] REDMON J, FARHADI A. YOLOv3: An incremental improve- ment [EB/OL]. (2018-04-08) [2021-08-01]. https://arxiv.org/abs/1804.02767.
    [3] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection [EB/OL]. (2020-04-23) [2021-08-01]. https://arxiv.org/abs/2004.10934.
    [4] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2016: 21-37.
    [5] LI X Y, LV Z G, WANG P, et al. Combination weighted clustering algorithms in cognitive radio networks[J]. Concurrency and Computation:Practice and Experience, 2020, 32(23): e5516.
    [6] 张云佐, 杨攀亮, 李汶轩. 基于改进SSD算法的铁路隧道漏缆卡具检测[J]. 激光与光电子学进展, 2021, 58(22): 2215005.

    ZHANG Y Z, YANG P L, LI W X. Leaky coaxial cable fixture detection based on improved SSD algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2215005(in Chinese).
    [7] ZHOU Q, ZHONG B, ZHANG Y, et al. Deep alignment network based multi-person tracking with occlusion and motion reasoning[J]. IEEE Transactions on Multimedia, 2018, 21(5): 1183-1194.
    [8] 崔家礼, 曹衡, 张亚明, 等. 一种复杂场景下的人眼检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 38-44. doi: 10.13700/j.bh.1001-5965.2019.0641

    CUI J L, CAO H, ZHANG Y M, et al. A human eye detection algorithm in complex scenarios[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 38-44(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0641
    [9] 符惠桐, 王鹏, 李晓艳, 等. 面向移动目标识别的轻量化网络模型[J]. 西安交通大学学报, 2021, 55(7): 1-9. doi: 10.7652/xjtuxb202107014

    FU H T, WANG P, LI X Y, et al. Lightweight network model for moving object recognition[J]. Journal of Xi’an Jiaotong University, 2021, 55(7): 1-9(in Chinese). doi: 10.7652/xjtuxb202107014
    [10] HOWARD A G, ZHU M, CHEN B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications [EB/OL]. (2017-04-17) [2021-08-01]. https://arxiv.org/abs/1704.04861.
    [11] MA N, ZHANG X, ZHENG H T, et al. ShuffleNet V2: Practical guidelines for efficient CNN architecture design[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 116-131.
    [12] TAN M, LE Q V. MixConv: Mixed depthwise convolutional kernels[EB/OL]. (2019-12-01) [2021-08-01]. https://arxiv.org/abs/1907.09595.
    [13] HAN K, WANG Y, TIAN Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 1580-1589.
    [14] SU Z, FANG L, KANG W, et al. Dynamic group convolution for accelerating convolutional neural networks[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2020: 138-155.
    [15] WEI H, WANG Z, HUA G. Dynamically mixed group convolution to lighten convolution operation[C]//2021 4th International Conference on Artificial Intelligence and Big Data. Piscataway: IEEE Press, 2021: 203-206.
    [16] CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 17355762.
    [17] XIE S, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 1492-1500.
    [18] 孙琪翔, 何宁, 张聪聪, 等. 轻量级图卷积人体骨架动作识别方法[J]. 计算机工程, 2022, 48(5): 306-313.

    SUN Q X, HE N, ZHANG C C, et al. A lightweight graph convolution human skeleton action recognition method[J]. Computer Engineering, 2022, 48(5): 306-313(in Chinese).
    [19] 罗禹杰, 张剑, 陈亮, 等. 基于自适应空间特征融合的轻量化目标检测算法设计[J]. 激光与光电子学进展, 2022, 59(4): 302-312.

    LUO Y J, ZHANG J, CHEN L, et al. Design of lightweight target detection algorithm based on adaptive spatial feature fusion[J]. Laser & Optoelectronics Progress, 2022, 59(4): 302-312(in Chinese).
    [20] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 390-391.
    [21] SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 4510-4520.
    [22] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Comp- uter Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 8759-8768.
    [23] 曹远杰, 高瑜翔. 基于GhostNet残差结构的轻量化饮料识别网络[J]. 计算机工程, 2022, 48(3): 310-314. doi: 10.19678/j.issn.1000-3428.0059966

    CAO Y J, GAO Y X. A lightweight beverage recognition network based on GhostNet residual structure[J]. Computer Engineering, 2022, 48(3): 310-314(in Chinese). doi: 10.19678/j.issn.1000-3428.0059966
  • 加载中
图(4) / 表(3)
计量
  • 文章访问数:  270
  • HTML全文浏览量:  69
  • PDF下载量:  37
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-10
  • 录用日期:  2021-10-09
  • 网络出版日期:  2021-11-02
  • 整期出版日期:  2023-06-30

目录

    /

    返回文章
    返回
    常见问答