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基于卷积神经网络的候选区域优化算法

王春哲 安军社 姜秀杰 邢笑雪

王春哲, 安军社, 姜秀杰, 邢笑雪. 基于卷积神经网络的候选区域优化算法[J]. 中国光学, 2019, 12(6): 1348-1361. doi: 10.3788/CO.20191206.1348
引用本文: 王春哲, 安军社, 姜秀杰, 邢笑雪. 基于卷积神经网络的候选区域优化算法[J]. 中国光学, 2019, 12(6): 1348-1361. doi: 10.3788/CO.20191206.1348
WANG Chun-zhe, AN Jun-she, JIANG Xiu-jie, XING Xiao-xue. Region proposal optimization algorithm based on convolutional neural networks[J]. Chinese Optics, 2019, 12(6): 1348-1361. doi: 10.3788/CO.20191206.1348
Citation: WANG Chun-zhe, AN Jun-she, JIANG Xiu-jie, XING Xiao-xue. Region proposal optimization algorithm based on convolutional neural networks[J]. Chinese Optics, 2019, 12(6): 1348-1361. doi: 10.3788/CO.20191206.1348

基于卷积神经网络的候选区域优化算法

doi: 10.3788/CO.20191206.1348
基金项目: 

国家自然科学基金 61805021

详细信息
    作者简介:

    王春哲(1989—), 男, 吉林松原人, 博士研究生, 2012年于长春大学获得学士学位, 2015年于长春理工大学获得硕士学位, 主要从事深度学习及目标检测方面的研究。E-mail:wangchunzhe163@sina.com

    安军社(1969—), 男, 陕西渭南人, 博士, 研究员, 1992年于北京航空航天大学获得学士学位, 1995年于北京科技大学获得硕士学位, 2004年于西北工业大学获得博士学位, 现为中国科学院国家空间科学中心研究员, 主要从事空间飞行器综合电子系统及深度学习方面的研究。E-mail:anjunshe@nssc.ac.cn

  • 中图分类号: TP394.1

Region proposal optimization algorithm based on convolutional neural networks

Funds: 

National Natural Science Foundation of China 61805021

More Information
  • 摘要: 在目标检测中,通常使用候选区域提高目标的检测效率。为解决当前候选区域质量较低的问题,本文将卷积边缘特征、显著性及目标位置信息引入到候选区域算法中。首先,利用卷积神经网络将待检测图像生成更富有语义信息的边缘特征,并通过边缘点聚合及边缘组相似性策略,获取每个滑动窗口的边缘信息得分;其次,利用显著性目标的局部特征,统计每个滑动窗口中的目标显著性得分;第三,根据目标可能出现的位置,计算每个滑动窗口中的目标位置信息得分;最后,利用边缘信息、显著性及位置信息的分数确定候选区域。在PASCAL VOC 2007验证集上进行实验,给定10 000个候选区域,交并比取0.7时,所提算法的召回率为90.50%,较Edge Boxes算法提高了3%。每张图像的运行时间大约为0.76 s。结果表明,本文算法可快速产生较高质量的候选区域。
  • 图  1  所提算法实现框图

    Figure  1.  Block diagram of the proposed algorithm

    图  2  RCF结构

    Figure  2.  The structure of RCF

    图  3  给定一张图像X

    Figure  3.  An given image X

    图  4  X的边缘特征图

    Figure  4.  Edge feature maps of X

    图  5  图像块的卡方距离

    Figure  5.  The chi-square distance of image patches

    图  6  选取S图像块的策略

    Figure  6.  Selection strategy of S image patch

    图  7  目标位置与目标数目关系。(a)VOC 2007数据集;(b)VOC 2012数据集

    Figure  7.  Relationship between the object′s location and object′s number. (a) VOC 2007 dataset; (b) VOC 2012 dataset

    图  8  参数αβ与召回率的关系

    Figure  8.  Relationship of the parameters α, β and recall

    图  9  参数w与召回率的关系

    Figure  9.  Relationship of the parameter w and recall

    图  10  不同候选框数下召回率与交并比之间的关系

    Figure  10.  Relationship between recall and IoU at different number of proposals

    图  11  不同交并比的候选框数与召回率的关系

    Figure  11.  Recall versus number of proposals at different IoUs

    图  12  13种算法不同位置目标的召回率与交并比的关系

    Figure  12.  Recall vs IoU curves of objects at different locations by 13 kinds of algorithms

    图  13  不同交并比下候选框数与召回率的关系

    Figure  13.  Recall versus number of proposals at different IoUs

    图  14  本文算法在PASCAL VOC 2007测试集的召回率

    Figure  14.  Recall on the PASCAL VOC 2007 test set for proposed algorithm in this paper

    图  15  不同宽高比时测试集及验证集上的召回率

    Figure  15.  The recalls at different aspect ratios of test set and validation set

    图  16  所提算法对部分目标的检测结果

    Figure  16.  Object detection results of some objects detected by proposed algorithm

    图  17  漏检目标的尺寸与漏检目标数目间的关系

    Figure  17.  The relation of the size of undetected objects and the number of undetected objects

    表  1  边缘组算法描述

    Table  1.   The description of edge group algorithm

    下载: 导出CSV

    表  2  精调滑动窗口策略

    Table  2.   The strategy of refining sliding windows

    下载: 导出CSV

    表  3  VOC 2007数据集特性

    Table  3.   The properties of VOC 2007 dataset

    数据集 训练集 验证集 测试集
    图像数 2 501 2 510 4 952
    目标数 6 301 6 307 12 032
    下载: 导出CSV

    表  4  交并比为0.7时13种算法的实验结果

    Table  4.   The experiment results of 13 kinds of algorithms with IoU of 0.7

    Algorithms AUC 45% 60% 75% R1000 R2000 R10000 mAP t/s
    Object-ness 0.27 -- -- -- 37.68% 37.89% 37.93% 51.4 3
    BING 0.20 -- -- -- 27.04% 27.39% 28.14% 49.0 0.2
    CPMC 0.41 86 475 -- 62.58% 62.59% 62.60% 57.1 250
    SS 0.40 171 530 1 812 68.13% 76.13% 89.12% 59.5 10
    EB 0.46 77 234 804 77.39% 83.25% 87.19% 60.4 0.25
    Rantalankila 0.23 489 1 712 -- 55.79% 61.21% 68.94% 57.9 10
    Rand. Prim′s 0.35 274 950 4 095 60.61% 68.52% 79.33% 57.6 1
    MCG 0.48 60 240 1 116 74.14% 79.58% 80.53% 60.3 30
    Endres 0.44 75 432 -- 63.93% 64.69% 64.88% 57.4 100
    Geodesic 0.35 266 630 2 491 66.45% 73.65% 81.05% 57.5 1
    Rigor 0.30 600 997 1 948 60.08% 75.59% 75.77% 58.4 10
    Improved EdgeBoxes 0.46 80 265 802 77.50% 84.15% 89.25% 60.8 0.43
    本文算法 0.47 103 276 799 77.87% 84.73% 90.50% 61.3 0.764 9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-28
  • 修回日期:  2019-06-14
  • 刊出日期:  2019-12-01

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