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基于注意力机制和循环域三元损失的域适应目标检测

周洋 韩冰 高新波 杨铮 陈玮铭

周洋, 韩冰, 高新波, 杨铮, 陈玮铭. 基于注意力机制和循环域三元损失的域适应目标检测. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220938
引用本文: 周洋, 韩冰, 高新波, 杨铮, 陈玮铭. 基于注意力机制和循环域三元损失的域适应目标检测. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220938
Zhou Yang, Han Bing, Gao Xin-Bo, Yang Zheng, Chen Wei-Ming. Domain adaptive object detection based on attention mechanism and cycle domain triplet loss. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220938
Citation: Zhou Yang, Han Bing, Gao Xin-Bo, Yang Zheng, Chen Wei-Ming. Domain adaptive object detection based on attention mechanism and cycle domain triplet loss. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220938

基于注意力机制和循环域三元损失的域适应目标检测

doi: 10.16383/j.aas.c220938
基金项目: 国家自然科学基金(62076190, 41831072, 41874195, 62036007), 陕西省重点创新产业链(2022ZDLGY01-11)资助
详细信息
    作者简介:

    周洋:西安电子科技大学电子工程学院硕士研究生. 2020获西南石油大学电子信息工程学士学位. 主要研究方向为计算机视觉和域适应目标检测. E-mail: yzhou_6@stu.xidian.edu.cn

    韩冰:博士, 西安电子科技大学电子工程学院教授. 目前主要研究领域为智能辅助驾驶、视觉感知与认知、空间物理与人工智能交叉研究等. 本文通信作者. E-mail: bhan@xidian.edu.cn

    高新波:博士, 西安电子科技大学教授, 重庆邮电大学校长. 目前主要从事机器学习、图像处理、计算机视觉、模式识别和多媒体内容分析等领域的研究. E-mail: xbgao@ieee.org

    杨铮:西安电子科技大学电子工程学院博士研究生. 2017获西安电子科技大学智能科学与技术学士学位. 主要研究方向为深度学习, 目标跟踪和强化学习. E-mail: zhengy@stu.xidian.edu.cn

    陈玮铭:西安电子科技大学电子工程学院硕士研究生. 2019获西安电子科技大学机械设计制造及其自动化学士学位. 主要研究方向为计算机视觉, 目标检测和遥感技术. E-mail: wmchen@stu.xidian.edu.cn

Domain Adaptive Object Detection Based on Attention Mechanism and Cycle Domain Triplet Loss

Funds: Supported by National Natural Science Foundation of China (62076190,41831072, 41874195, 62036007), The Key Industry Innovation Chain of Shaanxi under Grant (2022ZDLGY01-11)
More Information
    Author Bio:

    ZHOU Yang Master student at the School of Electronic Engineering, Xidian University. He received his bachelor degree in electronic and information engineering from Southwest Petroleum University in 2020. His research interest covers computer vision and domain adaptive detection

    HAN Bing Professor at school of Electronic Engineering, Xidian University. Her research interest covers intelligent auxiliary drive syste, visual perception and cognition, and cross-disciplinary research between space physics and pattern recognition. Corresponding author of this paper

    GAO Xin-bo Professor of Xidian University and the president of Chongqing University. His current research interests include Machine Learning, Image processing, computer vision, pattern recognition and multimedia analysis

    YANG Zheng Ph. D.candidate at the School of Electronic Engineering, Xidian University. He received his bachelor degree in intelligent science and technology from Xidian University in 2019. His research interests include deep learning, object tracking, and reinforcement learning

    CHEN Wei-Ming Master student at the School of Electronic Engineering, Xidian University. He received his bachelor degree in mechanical design manufacture and automation from Xidian University in 2019. His research interests include computer vision, object detection, and remote sensing

  • 摘要: 目前大多数深度学习算法都依赖于大量的标注数据并欠缺一定的泛化能力. 无监督域适应算法能提取到已标注数据和未标注数据间隐式共同特征, 从而提高算法在未标注数据上的性能. 目前域适应目标检测算法主要为两阶段目标检测器设计. 针对单阶段检测器中无法直接进行实例级特征对齐导致一定数量域不变特征的缺失, 提出结合通道注意力机制的图像级域分类器加强域不变特征提取. 此外对于域适应目标检测中存在类别特征的错误对齐引起的精度下降问题, 通过原型学习构建类别中心, 设计了一种基于原型的循环域三元损失函数, 从而实现原型引导的精细类别特征对齐. 以单阶段目标检测算法作为检测器, 在多种域适应目标检测公共数据集上进行实验. 实验结果证明该方法能有效提升原检测器在目标域的泛化能力达到更高的检测精度, 并且对于单阶段目标检测网络具有一定的通用性.
  • 图  1  基于注意力机制和循环域三元损失的域适应目标检测算法流程

    Fig.  1  The pipeline of domain adaptive object detection based on attention mechanism and cycle domain triplet loss

    图  2  循环域三元损失函数原理图

    Fig.  2  Principal of cycle domain adaptive tripleLoss

    图  3  本文方法在CityScapes→Foggy CityScapes上的主观检测结果

    Fig.  3  The Subjective results of the ours method on CityScapes→Foggy CityScapes

    图  4  本文方法在SunnyDay→DuskRainy和SunnyDay→NightRainy上的主观检测结果

    Fig.  4  The Subjective results of the ours method on SunnyDay→DuskRainy and SunnyDay→NightRainy

    图  5  本文方法在KITTI→CityScapes和Sim10k→CityScapes上的消融实验结果

    Fig.  5  The Subjective results of the ablation experiment on KITTI→CityScapes and Sim10k→CityScapes

    图  6  本文方法在VOC→Clipart1k上的主观结果

    Fig.  6  The Subjective results of ours on VOC→Clipart1k

    图  7  不同循环迭代训练次数在YOLOv3和YOLOv5检测器上的结果

    Fig.  7  The result of different cycle iteration on YOLOv3 and YOLOv5

    表  1  CityScapes→Foggy CityScapes数据集上的对比实验, “−”代表该方法没有进行此实验

    Table  1  The results of different methods on CityScapes→Foggy CityScapes, “−” represents the experiment is absent on this method

    方法检测器personridercartruckbusmotorbiketrainmAPmGP
    DAF[10]Faster-RCNN25.031.040.522.135.320.027.120.227.738.8
    SWDA[11]Faster-RCNN29.942.343.524.536.230.035.332.634.370.0
    C2F[14]Faster-RCNN34.046.952.130.843.234.737.429.938.679.1
    CAFA[16]Faster-RCNN41.938.756.722.641.524.635.526.836.081.9
    ICCR-VDD[21]Faster-RCNN33.444.051.733.952.034.236.834.740.0
    MeGA[20]Faster-RCNN37.749.052.425.449.234.539.046.941.891.1
    DAYOLO[28]YOLOv329.527.746.19.128.212.724.84.536.161.0
    本文方法(v3)YOLOv334.037.255.831.444.422.330.850.738.383.9
    MS-DAYOLO[31]YOLOv439.646.556.528.951.027.536.045.941.568.6
    A-DAYOLO[32]YOLOv532.835.751.318.834.511.825.616.228.3
    S-DAYOLO[34]YOLOv542.642.161.923.540.524.437.339.539.069.9
    本文方法(v5)YOLOv530.937.453.323.839.524.229.935.034.383.8
    下载: 导出CSV

    表  2  SunnyDay→DuskRainy数据集上的对比实验

    Table  2  The results of different methods on SunnyDay→DuskRainy

    方法检测器busbikecarmotorpersonridertruckmAP$\Delta$mAP
    DAF[10]Faster-RCNN43.627.552.316.128.521.744.833.55.2
    SWDA[11]Faster-RCNN40.022.851.415.426.320.344.231.53.2
    ICCR-VDD[21]Faster-RCNN47.933.255.126.130.523.848.137.89.5
    本文方法(v3)YOLOv350.124.970.724.239.119.053.240.27.4
    本文方法(v5)YOLOv546.222.168.216.534.817.550.536.59.4
    下载: 导出CSV

    表  3  SunnyDay→NightRainy数据集上的对比实验

    Table  3  The results of different methods on SunnyDay→NightRainy

    方法检测器busbikecarmotorpersonridertruckmAP$\Delta$mAP
    DAF[10]Faster-RCNN23.812.037.70.214.94.029.017.41.1
    SWDA[11]Faster-RCNN24.710.033.70.613.510.429.117.41.1
    ICCR-VDD[21]Faster-RCNN34.815.638.610.518.717.330.623.77.4
    本文方法(v3)YOLOv345.08.251.14.020.99.637.925.35.1
    本文方法(v5)YOLOv540.79.345.00.612.89.232.521.54.7
    下载: 导出CSV

    表  4  KITTI→CityScapes和Sim10k→CityScapes数据集上的对比实验, “−”代表该方法没有进行此实验

    Table  4  The results of different methods on KITTI→CityScapes and Sim10k→CityScapes, “−” represents the experiment is absent on this method

    方法KITTI→CityScapesSim10k→CityScapes
    APGPAPGP
    DAF[10]38.521.039.022.5
    SWDA[11]37.919.542.330.8
    C2F[14]43.835.3
    CAFA[16]43.232.949.047.7
    MeGA[20]43.032.444.837.0
    DAYOLO[28]54.082.250.939.5
    本文方法(v3)61.129.460.837.1
    A-DAYOLO[32]37.744.9
    S-DAYOLO[34]49.352.9
    本文方法(v5)60.050.460.356.3
    下载: 导出CSV

    表  5  CityScapes→FoggyCityscapes数据集上基于YOLOv3的消融实验

    Table  5  The results of ablation experiment on CityScapes→FoggyCityscapes based on YOLOv3

    方法personridercartruckbusmotorbiketrainmAP
    SO29.835.044.720.432.414.828.321.628.4
    CADC34.438.054.724.445.021.232.149.137.2
    CDTL31.138.046.728.934.523.427.813.730.5
    CADC+CDTL34.037.255.831.444.422.330.850.738.3
    Oracle34.938.855.925.345.022.633.449.140.2
    下载: 导出CSV

    表  6  CityScapes→FoggyCityscapes数据集上基于YOLOv5的消融实验

    Table  6  The results of ablation experiment on CityScapes→FoggyCityscapes based on YOLOv5

    方法personridercartruckbusmotorbiketrainmAP
    SO26.933.139.98.921.111.324.84.921.4
    CADC32.637.152.726.838.123.038.132.634.1
    CDTL29.736.743.213.125.517.128.713.126.2
    CADC+CDTL30.937.453.323.839.524.229.935.034.3
    Oracle34.837.957.524.442.723.133.240.836.8
    下载: 导出CSV

    表  7  SunnyDay→DuskRainy数据集上基于YOLOv3的消融实验

    Table  7  The results of ablation experiment on SunnyDay→DuskRainy based on YOLOv3

    方法busbikecarmotorpersonridertruckmAP
    Source Only43.714.368.412.031.510.948.732.8
    CADC50.022.670.823.238.418.7 53.539.6
    CDTL45.420.169.215.234.817.247.835.7
    CADC+ CDTL50.1 24.970.7 24.2 39.119.053.240.2
    下载: 导出CSV

    表  8  SunnyDay→DuskRainy数据集上基于YOLOv5的消融实验

    Table  8  The results of ablation experiment on SunnyDay→DuskRainy based on YOLOv5

    方法busbikecarmotorpersonridertruckmAP
    Source Only37.28.463.85.523.77.943.427.1
    CADC45.622.168.216.634.515.450.135.9
    CDTL41.613.165.57.629.710.244.930.4
    CADC+ CDTL46.222.1 68.2 16.534.817.550.5 36.5
    下载: 导出CSV

    表  9  SunnyDay→NightRainy数据集上基于YOLOv3的消融实验

    Table  9  The results of ablation experiment on SunnyDay→NightRainy based on YOLOv3

    方法busbikecarmotorpersonridertruckmAP
    Source Only39.25.144.20.214.86.930.720.2
    CADC44.48.150.90.620.2 11.338.324.8
    CDTL40.48.245.80.616.27.233.421.7
    CADC+ CDTL45.08.2 51.14.020.99.637.925.3
    下载: 导出CSV

    表  10  SunnyDay→NightRainy数据集上基于YOLOv5的消融实验

    Table  10  The results of ablation experiment on SunnyDay→NightRainy based on YOLOv5

    方法busbikecarmotorpersonridertruckmAP
    Source Only25.43.236.30.29.14.420.814.2
    CADC38.78.342.70.312.36.432.020.1
    CDTL34.36.244.20.511.28.730.319.3
    CADC+ CDTL40.79.345.0 0.6 12.8 9.232.5 21.5
    下载: 导出CSV

    表  11  KITTI→CityScapes和Sim10k→CityScapes数据集上的对比实验

    Table  11  The results of different methods on KITTI→CityScapes and Sim10k→CityScapes

    KITTISim10k
    YOLOv3
    Source Only59.658.5
    CADC60.559.6
    CDTL60.560.8
    CADC+ CDTL61.159.8
    Oracle64.764.7
    YOLOv5
    Source Only54.053.1
    CADC59.558.6
    CDTL59.060.3
    CADC+ CDTL60.059.0
    Oracle65.965.9
    下载: 导出CSV

    表  12  本文方法在VOC→Clipart1k上的实验

    Table  12  The experiment on VOC→Clipart1k

    方法aerobcyclebirdboatbottlebuscarcatchaircowtabledoghrsbikeprsnplntsheepsofatraintvmAP
    I3Net23.766.225.319.323.755.235.713.637.835.525.413.924.160.356.339.813.634.556.041.835.1
    I3Net+CDTL23.361.627.817.124.754.339.812.341.434.132.215.527.677.957.037.45.5031.351.847.836.0
    I3Net+CDTL+$CADC^*$31.260.431.819.427.063.340.713.741.138.427.218.025.567.854.937.215.536.454.847.837.6
    下载: 导出CSV

    表  13  本文方法在VOC→Comic2k上的实验

    Table  13  The experiment on VOC→Comic2k

    方法bikebirdcarcatdogpersonmAP
    I3Net44.917.831.910.723.546.329.2
    I3Net+CDTL43.715.131.511.718.646.927.9
    I3Net+CDTL+CADC*47.816.033.815.124.443.530.1
    下载: 导出CSV

    表  14  本文方法在VOC→Watercolor2k上的实验

    Table  14  The experiment on VOC→Watercolor2k

    方法bikebirdcarcatdogpersonmAP
    I3Net81.349.643.638.231.361.751.0
    I3Net+CDTL79.547.241.733.535.460.349.6
    I3Net+CDTL+CADC*84.145.346.632.931.461.450.3
    下载: 导出CSV

    表  15  像素级对齐对网络的影响

    Table  15  The impact of pixel alignment to network

    方法检测器C→FK→CS→C
    CDTL+CADCYOLOv335.959.858.4
    CDTL+CADC+$D_{pixel}$YOLOv337.260.559.6
    CDTL+CADCYOLOv532.758.956.8
    CDTL+CADC+$D_{pixel}$YOLOv534.159.558.6
    下载: 导出CSV

    表  16  通道注意力域分类器中损失函数的选择

    Table  16  The choice of loss function in channel attention domain classifier(CADC)

    检测器$F_1$$F_2$$F_3$mAP
    YOLOv3/v5CECECE35.8/32.7
    YOLOv3/v5CECEFL36.4/33.2
    YOLOv3/v5CEFLFL37.2/34.1
    YOLOv3/v5FLFLFL37.0/33.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-05
  • 录用日期:  2023-05-18
  • 网络出版日期:  2023-08-18

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