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集成多种上下文与混合交互的显著性目标检测

夏晨星 陈欣雨 孙延光 葛斌 方贤进 高修菊 张艳

夏晨星, 陈欣雨, 孙延光, 葛斌, 方贤进, 高修菊, 张艳. 集成多种上下文与混合交互的显著性目标检测[J]. 电子与信息学报. doi: 10.11999/JEIT230719
引用本文: 夏晨星, 陈欣雨, 孙延光, 葛斌, 方贤进, 高修菊, 张艳. 集成多种上下文与混合交互的显著性目标检测[J]. 电子与信息学报. doi: 10.11999/JEIT230719
XIA Chenxing, CHEN Xinyu, SUN Yanguang, GE Bin, FANG Xianjin, GAO Xiuju, ZHANG Yan. Integrating Multiple Context and Hybrid Interaction for Salient Object Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230719
Citation: XIA Chenxing, CHEN Xinyu, SUN Yanguang, GE Bin, FANG Xianjin, GAO Xiuju, ZHANG Yan. Integrating Multiple Context and Hybrid Interaction for Salient Object Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230719

集成多种上下文与混合交互的显著性目标检测

doi: 10.11999/JEIT230719
基金项目: 国家自然科学基金(62102003),安徽省自然科学基金(2108085QF258),安徽省博士后基金(2022B623),淮南市科技计划项目(2023A316),安徽高校协同创新项目(GXXT-2021-006, GXXT-2022-038),安徽理工大学青年科学研究基金一般项目(xjyb2020-04),中央引导地方科技发展专项资金(202107d06020001)
详细信息
    作者简介:

    夏晨星:男,副教授,研究方向为计算机视觉

    陈欣雨:女,硕士生,研究方向为伪装目标检测

    孙延光:男,博士生,研究方向为显著性目标检测

    葛斌:男,教授,研究方向为图像加密、计算机视觉

    方贤进:男,教授,研究方向为人工智能、信息安全

    高修菊:女,讲师,研究方向为计算机视觉

    张艳:女,副教授,研究方向为模式识别与图像处理

    通讯作者:

    陈欣雨 18900573647@163.com

  • 中图分类号: TP391.41

Integrating Multiple Context and Hybrid Interaction for Salient Object Detection

Funds: The National Natural Science Foundation of China (62102003), The Natural Science Foundation of Anhui Province (2108085QF258), Anhui Postdoctoral Science Foundation (2022B623), Huainan City Science and Technology Plan Project (2023A316), The University Synergy Innovation Program of Anhui Province (GXXT-2021-006, GXXT-2022-038), The University-level General Projects of Anhui University of Science and Technology (xjyb2020-04), The Central Guiding Local Technology Development Special Funds (202107d06020001)
  • 摘要: 显著性目标检测目的是识别和分割图像中的视觉显著性目标,它是计算机视觉任务及其相关领域的重要研究内容之一。当下基于全卷积网络(FCNs)的显著性目标检测方法已经取得了不错的性能,然而现实场景中的显著性目标类型多变且尺寸不固定,这使得准确检测并完整分割出显著性目标仍然是一个巨大的挑战。为此,该文提出集成多种上下文和混合交互的显著性目标检测方法,通过利用密集上下文信息探索模块和多源特征混合交互模块来高效预测显著性目标。密集上下文信息探索模块采用空洞卷积、不对称卷积和密集引导连接渐进地捕获具有强关联性的多尺度和多感受野上下文信息,通过集成这些信息来增强每个初始多层级特征的表达能力。多源特征混合交互模块包含多种特征聚合操作,可以自适应交互来自多层级特征中的互补性信息,以生成用于准确预测显著性图的高质量特征表示。此方法在5个公共数据集上进行了性能测试,实验结果表明,该文方法在不同的评估指标下与19种基于深度学习的显著性目标检测方法相比取得优越的预测性能。
  • 图  1  一些方法与本文方法的预测显著性图

    图  2  本文显著性目标检测方法的完整流程图

    图  3  密集上下文信息探索模块

    图  4  多源特征混合交互模块

    图  5  不同显著性目标检测方法的PR曲线比较

    图  7  不同显著性目标检测方法的Fm曲线比较

    图  6  本文方法与10种最近的SOD方法进行视觉比较结果

    图  8  本文方法使用不同模块定性比较结果

    图  9  MFHI模块不同聚合策略定性比较结果

    图  10  不同模块预测显著性图比较

    图  11  DCIE模块不同结构预测显著性图比较

    表  1  MAE, AFm和WFm的定量比较结果

    方法 ECSSD (1000) PASCAL-S (850) HKU-IS (4447) DUT-OMRON (5168) DUTS-TE (5019)
    MAE AFm WFm MAE AFm WFm MAE AFm WFm MAE AFm WFm MAE AFm WFm
    Amulet17 0.059 0.868 0.840 0.100 0.757 0.728 0.051 0.841 0.817 0.098 0.647 0.626 0.085 0.678 0.658
    UCF17 0.069 0.844 0.806 0.116 0.726 0.726 0.062 0.823 0.779 0.120 0.621 0.574 0.112 0.631 0.596
    DGRL18 0.046 0.893 0.871 0.077 0.794 0.772 0.041 0.875 0.851 0.066 0.711 0.688 0.054 0.755 0.748
    BDMPM18 0.045 0.869 0.871 0.074 0.758 0.774 0.039 0.871 0.859 0.064 0.692 0.681 0.049 0.746 0.761
    PoolNet19 0.039 0.915 0.896 0.075 0.815 0.793 0.032 0.900 0.883 0.056 0.739 0.721 0.040 0.809 0.807
    CPD19 0.037 0.917 0.898 0.071 0.820 0.794 0.033 0.895 0.879 0.056 0.747 0.719 0.043 0.805 0.795
    AFNet19 0.042 0.908 0.886 0.070 0.815 0.792 0.036 0.888 0.869 0.057 0.739 0.717 0.046 0.793 0.785
    R2Net20 0.038 0.914 0.899 0.069 0.817 0.793 0.033 0.896 0.880 0.054 0.744 0.728 0.041 0.801 0.804
    GateNet20 0.040 0.916 0.894 0.067 0.819 0.797 0.033 0.899 0.880 0.055 0.746 0.729 0.040 0.807 0.809
    ITSD20 0.035 0.895 0.911 0.066 0.785 0.812 0.031 0.899 0.894 0.061 0.756 0.750 0.041 0.804 0.824
    MINet20 0.034 0.924 0.911 0.064 0.829 0.809 0.029 0.909 0.897 0.056 0.756 0.738 0.037 0.828 0.825
    SUCA21 0.036 0.915 0.906 0.067 0.818 0.803 0.031 0.897 0.890 0.044 0.803 0.802
    CANet21 0.044 0.900 0.878 0.073 0.813 0.792 0.037 0.882 0.866 0.058 0.731 0.720 0.044 0.785 0.788
    DSRNet21 0.039 0.910 0.891 0.067 0.819 0.801 0.035 0.893 0.873 0.061 0.727 0.711 0.043 0.791 0.794
    VST21 0.033 0.920 0.910 0.061 0.829 0.816 0.029 0.900 0.897 0.058 0.756 0.755 0.037 0.818 0.828
    DNA22 0.042 0.891 0.883 0.079 0.790 0.772 0.035 0.863 0.864 0.063 0.694 0.696 0.046 0.747 0.765
    DCENet22 0.035 0.926 0.913 0.061 0.845 0.825 0.029 0.908 0.898 0.055 0.771 0.754 0.038 0.842 0.834
    DNTDF22 0.034 0.900 0.909 0.064 0.810 0.814 0.028 0.905 0.901 0.051 0.748 0.732 0.033 0.822 0.839
    ICON23 0.032 0.928 0.918 0.064 0.833 0.818 0.029 0.910 0.902 0.057 0.772 0.761 0.037 0.838 0.837
    本文 0.032 0.935 0.922 0.060 0.841 0.822 0.026 0.923 0.911 0.049 0.778 0.759 0.034 0.862 0.846
    下载: 导出CSV

    表  2  参数量、推理速度和模型内存的比较结果

    方法输入尺寸参数量 (M)推理速度 (帧/s)模型内存 (MB)
    Amulet320×32033.158132
    DGRL384×384161.748631
    BDMPM256×256-22259
    PoolNet384×38468.2617410
    GateNet384×384128.6330503
    MINet320×320162.3825635
    DSRNet400×40075.2915290
    本文320×32029.9726117
    下载: 导出CSV

    表  3  本文方法使用不同模块定量比较结果

    方法 HKU-IS (4447) DUTS-TE (5019)
    MAE AFm WFm MAE AFm WFm
    Res 0.042 0.866 0.843 0.053 0.772 0.760
    Res+FPN 0.037 0.884 0.870 0.045 0.800 0.784
    Res+DCIE+FPN 0.028 0.913 0.900 0.037 0.845 0.828
    Res+DCIE+MFHI 0.026 0.923 0.911 0.034 0.862 0.846
    下载: 导出CSV

    表  4  MFHI模块不同聚合策略定量比较结果

    方法 HKU-IS (4447) DUTS-TE (5019)
    MAE AFm WFm MAE AFm WFm
    Res+MFHI(cat) 0.030 0.903 0.892 0.041 0.822 0.813
    Res+MFHI(mul) 0.032 0.899 0.888 0.041 0.818 0.811
    Res+MFHI(add) 0.033 0.894 0.884 0.042 0.818 0.803
    Res+MFHI(h1) 0.030 0.903 0.892 0.039 0.825 0.815
    Res+MFHI(h2) 0.031 0.902 0.891 0.039 0.824 0.815
    Res+MFHI 0.029 0.906 0.898 0.039 0.832 0.819
    下载: 导出CSV

    表  5  不同模块对比测试

    方法 HKU-IS (4447) DUTS-TE (5019)
    MAE AFm WFm MAE AFm WFm
    Res+ASPP+FPN 0.031 0.905 0.892 0.040 0.825 0.815
    Res+RFB+FPN 0.031 0.903 0.891 0.039 0.828 0.818
    Res+PDC+FPN 0.032 0.899 0.899 0.040 0.826 0.813
    Res+DCIE+FPN 0.028 0.913 0.900 0.037 0.845 0.828
    下载: 导出CSV

    表  6  DCIE模块消融分析

    方法HKU-IS (4447)DUTS-TE (5019)
    MAEAFmWFmMAEAFmWFm
    Res+DCIE(w D)+FPN0.0320.8980.8860.0390.8230.815
    Res+DCIE(w A)+FPN0.0320.8980.8860.0400.8220.814
    Res+DCIE(w D+A)+FPN0.0300.9070.8950.0380.8390.823
    Res+DCIE+FPN0.0280.9130.9000.0370.8450.828
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-18
  • 修回日期:  2024-01-06
  • 网络出版日期:  2024-01-28

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