多尺度注意力学习的Faster R-CNN口罩人脸检测模型

李泽琛; 李恒超; 胡文帅; 杨金玉; 华泽玺

doi:10.3969/j.issn.0258-2724.20210017

多尺度注意力学习的Faster R-CNN口罩人脸检测模型

doi: 10.3969/j.issn.0258-2724.20210017

基金项目: 国家自然科学基金（61871335）；中央高校基本业务费专项资金（2682020XG02，2682020ZT35）；国家重点研发计划（2020YFB1711902）

详细信息

作者简介:
李泽琛（1996—），男，博士研究生，研究方向为图像处理与模式识别，E-mail：Lizc@my.swjtu.edu.cn

通讯作者:
华泽玺（1968—），男，副教授，博士，研究方向为轨道交通智慧运维、传感器与智能检测、监测，E-mail：huazexi@163.com

中图分类号: TP391.41；TP183
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- 文章访问数: 710
- HTML全文浏览量: 375
- PDF下载量: 67
- 被引次数: 59
出版历程
- 收稿日期: 2021-01-11
- 修回日期: 2021-07-07
- 网络出版日期: 2021-07-16
- 刊出日期: 2021-10-15

Masked Face Detection Model Based on Multi-scale Attention-Driven Faster R-CNN

摘要

摘要: 针对在佩戴口罩等有遮挡条件下的人脸检测问题，提出了多尺度注意力学习的Faster R-CNN （MSAF R-CNN）人脸检测模型. 首先，为充分考虑人脸目标多尺度信息，相较于原始Faster R-CNN框架，引入Res2Net分组残差结构，获取更细粒度的特征表征；其次，基于空间-通道注意力结构改进的Res2Net模块，结合注意力机制自适应学习目标不同尺度特征；最后，为学习目标的全局信息并减轻过拟合现象，在模型顶端嵌入加权空间金字塔池化网络，采用由粗到细的方式进行特征尺度划分. 在AIZOO和FMDD两个人脸数据集上的实验结果表明：所提出MSAF R-CNN模型对佩戴口罩的人脸检测准确率分别达到90.37%和90.11%，验证了模型的可行性和有效性.
- 口罩人脸 /
- 深度学习 /
- 注意力机制 /
- 多尺度学习 /
- 特征融合 /
- 目标检测
Abstract: For the purpose of masked face detection, a multi-scale attention-driven faster region-based convolutional neural network (MSAF R-CNN) model is proposed. First, given the Faster R-CNN model architecture and the multi-scale information of the face, Res2Net, a grouped-residual structure, is introduced to model more fine-grained features. Then, inspired by the attention mechanism, a novel spatial-channel attention Res2Net (SCA-Res2Net) module is developed to learn the multi-scale features adaptively. Finally, to further learn the global feature representation and ease the overfitting problem, the weighted spatial pyramid pooling network is embedded on the top of the model, which can segment the feature maps into different groups from finer to coarser scales. Experimental results on the AIZOO and FMDD datasets show that the accuracy of masked face detection with the proposed MSAF R-CNN model can reach 90.37% and 90.11%, respectively, thus verifying the feasibility and effectiveness of the proposed model.
- masked face /
- deep learning /
- attention mechanism /
- multi-scale learning /
- feature fusion /
- object detection

HTML全文

图 1 Res2Net模块

Figure 1. Res2Net module

下载: 全尺寸图片幻灯片

图 2 SCA-Res2Net模块

Figure 2. Structure of SCA-Res2Net module

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图 3 WSPP-Net模块

Figure 3. Structure of WSPP-Net

下载: 全尺寸图片幻灯片

图 4 MSAF R-CNN模型

注： BN —batch normalization

Figure 4. MSAF R-CNN model

下载: 全尺寸图片幻灯片

图 5 数据集部分图像

Figure 5. Partial images of datasets

下载: 全尺寸图片幻灯片

表 1 不同分组数实验结果

Table 1. Experimental results under different numbers of groups %

数据集	类别	分组数
数据集	类别	2	4	6	8	10
AIZOO	Face	90.43	90.32	90.11	89.92	90.10
	Mask	89.95	90.37	89.86	90.27	89.50
	mAP	90.19	90.35	89.99	90.10	89.80
FMDD	Face	86.21	87.27	86.17	86.50	86.17
	Mask	89.99	90.11	90.04	90.21	89.99
	mAP	88.10	88.69	88.10	88.35	88.08

下载: 导出CSV

表 2 不同压缩比实验结果

Table 2. Experimental results under different compression ratios %

数据集	类别	压缩比
数据集	类别	10	12	14	16	18
AIZOO	Face	90.31	90.39	90.12	90.32	90.41
	Mask	89.79	90.08	90.20	90.37	89.87
	mAP	90.05	90.23	90.16	90.35	90.14
FMDD	Face	86.98	84.89	86.26	87.27	86.30
	Mask	89.90	89.68	90.25	90.11	89.86
	mAP	88.44	87.29	88.26	88.69	88.08

下载: 导出CSV

表 3 WSPP-Net不同多尺度窗口大小实验结果

Table 3. Experimental results under different window sizes in WSPP-Net %

数据集	类别	窗口大小
数据集	类别	S1	S2	S3	S4
AIZOO	Face	90.08	90.32	90.32	90.38
	Mask	90.31	90.37	90.13	90.01
	mAP	90.20	90.35	90.22	90.19
FMDD	Face	86.51	87.27	86.56	86.45
	Mask	89.76	90.11	89.60	89.99
	mAP	88.14	88.69	88.08	88.22

下载: 导出CSV

表 4 不同检测方法的性能

Table 4. Performance of different methods %

数据集	类别	模型 1	模型 2	模型 3	模型 4	MSAF R-CNN
AIZOO	Face	87.32	90.42	89.94	90.19	90.32
	Mask	78.15	89.84	89.71	89.99	90.37
	mAP	82.73	90.13	89.82	90.09	90.35
FMDD	Face	86.01	86.41	84.44	85.05	87.27
	Mask	77.95	90.01	89.94	90.10	90.11
	mAP	81.98	88.21	87.19	87.58	88.69

下载: 导出CSV

表 5 消融实验结果

Table 5. Ablation experimental results of feature removal and fusion %

数据集	类别	模型 5	模型 6	模型 7	MSAF R-CNN
AIZOO	Face	90.43	89.92	90.40	90.32
	Mask	90.05	90.03	90.00	90.37
	mAP	90.24	89.97	90.20	90.35
FMDD	Face	85.13	86.01	86.23	87.27
	Mask	89.93	90.00	89.98	90.11
	mAP	87.53	88.01	88.10	88.69

下载: 导出CSV

参考文献(29)

VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. Kauai: IEEE, 2001: I.511-I.518.

LIENHART R, MAYDT J. An extended set of Haar-like features for rapid object detection[C]//Proceedings of International Conference on Image Processing. New York: IEEE, 2002: I.900-I.903.

胡丽乔,仇润鹤. 一种自适应加权HOG特征的人脸识别算法[J]. 计算机工程与应用,2017,53(3): 164-168. doi: 10.3778/j.issn.1002-8331.1506-0183

HU Liqiao, QIU Runhe. Face recognition based on adaptively weighted HOG[J]. Computer Engineering and Applications, 2017, 53(3): 164-168. doi: 10.3778/j.issn.1002-8331.1506-0183

张路达,邓超. 多尺度融合的YOLOv3人群口罩佩戴检测方法[J]. 计算机工程与应用,2021,57(16): 283-290.

ZHANG Luda, DENG Chao. Multi-scale fusion of YOLOv3 crowd mask wearing detection method[J]. Computer Engineering and Applications, 2021, 57(16): 283-290.

魏丽,王洁,姜昕言,等. 遮挡条件下的人脸检测与遮挡物属性判识[J]. 计算机仿真,2020,37(9): 441-445,450. doi: 10.3969/j.issn.1006-9348.2020.09.093

WEI Li, WANG Jie, JIANG Xinyan, et al. Face detection and obstacle attribute identification under occlusion[J]. Computer Simulation, 2020, 37(9): 441-445,450. doi: 10.3969/j.issn.1006-9348.2020.09.093

薛均晓,程君进,张其斌,等. 改进轻量级卷积神经网络的复杂场景口罩佩戴检测方法[J]. 计算机辅助设计与图形学学报,2021,33(7): 1045-1054.

XUE Junxiao, CHENG Junjin, ZHANG Qibin, et al. Improved efficient convolutional neural network for complex scene mask-wearing detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(7): 1045-1054.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M]//Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.

迟万达,王士奇,张潇,等. 基于轻量化SSD的人脸检测模型设计[J]. 计算机与网络,2021,47(5): 69-73. doi: 10.3969/j.issn.1008-1739.2021.05.055

CHI Wanda, WANG Shiqi, ZHANG Xiao, et al. Design on face detection model based on lightweight SSD[J]. Computer & Network, 2021, 47(5): 69-73. doi: 10.3969/j.issn.1008-1739.2021.05.055

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.

GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1440-1448.

REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031

GAO S H, CHENG M M, ZHAO K, et al. Res2Net:a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2): 652-662. doi: 10.1109/TPAMI.2019.2938758

BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[EB/OL]. (2014-09-01)[2020-12-20]. https://www.researchgate.net/publication/265252627_Neural_Machine_Translation_by_Jointly_Learning_to_Align_and_Translate.

ZHU Y S, ZHAO C Y, GUO H Y, et al. Attention coupleNet:fully convolutional attention coupling network for object detection[J]. IEEE Transactions on Image Processing, 2019, 28(1): 113-126. doi: 10.1109/TIP.2018.2865280

ZHANG J F, NIU L, ZHANG L Q. Person re-identification with reinforced attribute attention selection[J]. IEEE Transactions on Image Processing, 2021, 30: 603-616. doi: 10.1109/TIP.2020.3036762

HE L, CHAN J C W, WANG Z M. Automatic depression recognition using CNN with attention mechanism from videos[J]. Neurocomputing, 2021, 422: 165-175. doi: 10.1016/j.neucom.2020.10.015

HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.

MUDUMBI T, BIAN N Z, ZHANG Y Y, et al. An approach combined the faster RCNN and mobilenet for logo detection[J]. Journal of Physics:Conference Series, 2019, 1284: 012072.1-012072.8.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations. San Diego: [s.n.], 2015: 1-14.

SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Boston: IEEE, 2015: 1-9.

HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.

WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.

XI O Y, KANG G, PAN Z. Spatial pyramid pooling mechanism in 3D convolutional network for sentence-level classification[J]. IEEE/ACM Transactions on Audio,Speech,and Language Processing, 2018, 26(11): 2167-2179. doi: 10.1109/TASLP.2018.2852502

YANG R, ZHANG Y, ZHAO P F, et al. MSPPF-nets: a deep learning architecture for remote sensing image classification[C]//IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 3045-3048.

WANG H J, SHI Y Y, YUE Y J, et al. Study on freshwater fish image recognition integrating SPP and DenseNet network[C]//2020 IEEE International Conference on Mechatronics and Automation (ICMA). Beijing: IEEE, 2020: 564-569.

WANG T, YUAN L, ZHANG X, et al. Distilling object detectors with fine-grained feature imitation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4928-4937.

YANG S, LUO P, LOY C C, et al. WIDER FACE: a face detection benchmark[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 5525-5533.

GE S M, LI J, YE Q T, et al. Detecting masked faces in the wild with LLE-CNNs[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 426-434.

LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2999-3007.

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