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基于注意力机制的多尺度全场景监控目标检测方法

张德祥 王俊 袁培成

张德祥, 王俊, 袁培成. 基于注意力机制的多尺度全场景监控目标检测方法[J]. 电子与信息学报, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664
引用本文: 张德祥, 王俊, 袁培成. 基于注意力机制的多尺度全场景监控目标检测方法[J]. 电子与信息学报, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664
ZHANG Dexiang, WANG Jun, YUAN Peicheng. Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664
Citation: ZHANG Dexiang, WANG Jun, YUAN Peicheng. Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664

基于注意力机制的多尺度全场景监控目标检测方法

doi: 10.11999/JEIT210664
基金项目: 国家重点研发计划(2018YFB0504604)
详细信息
    作者简介:

    张德祥:男,教授,研究方向为遥感图像处理、深度学习

    王俊:男,硕士生,研究方向为视频与图像处理、机器学习

    袁培成:男,硕士生,研究方向为视频与图像处理、机器学习

    通讯作者:

    张德祥 dqxyzdx@126.com

  • 中图分类号: TN911.73; TP391.4

Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism

Funds: The Foundation National Key Research and Development Program (2018YFB0504604)
  • 摘要: 针对复杂城市监控场景中由于目标尺寸变化大、目标遮挡、天气影响等原因导致目标特征不明显的问题,该文提出一种基于注意力机制的多尺度全场景监控目标检测方法。该文设计了一种基于Yolov5s模型的多尺度检测网络结构,以提高网络对目标尺寸变化的适应性。同时,构建了基于注意力机制的特征提取模块,通过网络学习获得特征的通道级别权重,增强了目标特征,抑制了背景特征,提高了特征的网络提取能力。通过K-means聚类算法计算全场景监控数据集的初始锚框大小,加速模型收敛同时提升检测精度。在COCO数据集上,与基本网络相比,平均精度均值(mAP)提高了3.7%,mAP50提升了4.7%,模型推理时间仅为3.8 ms。在整个场景监控数据集中,mAP50达到89.6%,处理监控视频时为154 fps,满足监控现场的实时检测要求。
  • 图  1  Yolov5s网络结构

    图  2  多尺度检测网络结构

    图  3  SE模块结构图

    图  4  SE-CSPNet

    图  5  数据集示例图片

    图  6  检测结果对比

    表  1  COCO数据集上的消融实验结果

    方法mAP50mAP推理时间(ms)
    Yolov5s55.436.73.0
    Yolov5s+Attention155.735.43.3
    Yolov5s +Attention256.837.43.2
    Yolov5s +Attention353.133.02.9
    Yolov5s +Attention454.233.52.8
    Yolov5s +Multi-scale59.039.33.5
    MODN-BAM60.140.43.8
    下载: 导出CSV

    表  2  Open Images v6数据集上的消融实验结果

    方法mAP50mAP
    Yolov5s71.249.8
    Yolov5s+Attention272.151.3
    Yolov5s+ Multi-scale74.655.5
    MODN-BAM75.756.3
    下载: 导出CSV

    表  3  COCO数据集上与其他算法的对比结果

    方法SizemAPmAP50mAP75mAPsmAPmmAPlfps
    RetinaNet-ResNet101800×80037.857.540.820.241.149.211
    YOLOF800×*37.756.940.619.142.553.232
    YOLOF-ResNet101800×*39.859.442.920.544.554.921
    RDSNet800×80038.158.540.821.241.548.210.9
    Yolov3608×60833.057.934.418.335.441.920
    Yolov3-SPP608×60836.260.638.220.637.446.173
    NAS-FPN640×64039.924
    EfficientDet-D1640×64039.658.642.317.944.356.050
    Yolov5s640×64036.755.439.021.141.945.5204
    MODN-BAM640×64040.460.143.322.545.054.9175
    下载: 导出CSV

    表  4  全场景监控数据集上的消融实验结果

    方法frame sizemAP50fps
    Yolov5s1920×108085.7182
    Yolov5s+Attention21920×108087.6176
    Yolov5s+ Multi-scale1920×108088.4163
    MODN-BAM1920×108089.6154
    下载: 导出CSV
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  • 被引次数: 0
出版历程
  • 收稿日期:  2021-07-02
  • 录用日期:  2022-01-12
  • 修回日期:  2021-12-25
  • 网络出版日期:  2022-02-03
  • 刊出日期:  2022-09-19

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