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.
李泽琛, 李恒超, 胡文帅, 杨金玉, 华泽玺. 多尺度注意力学习的Faster R-CNN口罩人脸检测模型[J]. 西南交通大学学报, 2021, 56(5): 1002-1010.
LI Zechen, LI Hengchao, HU Wenshuai, YANG Jinyu, HUA Zexi. Masked Face Detection Model Based on Multi-scale Attention-Driven Faster R-CNN. Journal of SouthWest JiaoTong University, 2021, 56(5): 1002-1010.
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.
[2]
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.
[3]
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): 164-168HU Liqiao, QIU Runhe. Face recognition based on adaptively weighted HOG[J]. Computer Engineering and Applications, 2017, 53(3): 164-168
[4]
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): 283-290ZHANG Luda, DENG Chao. Multi-scale fusion of YOLOv3 crowd mask wearing detection method[J]. Computer Engineering and Applications, 2021, 57(16): 283-290
[5]
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): 441-445,450WEI Li, WANG Jie, JIANG Xinyan, et al. Face detection and obstacle attribute identification under occlusion[J]. Computer Simulation, 2020, 37(9): 441-445,450
[6]
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): 1045-1054XUE 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
[7]
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.
[8]
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): 69-73CHI Wanda, WANG Shiqi, ZHANG Xiao, et al. Design on face detection model based on lightweight SSD[J]. Computer & Network, 2021, 47(5): 69-73
[9]
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.
[10]
GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago： IEEE，2015： 1440-1448.
[11]
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
[12]
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
[13]
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.
[14]
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
[15]
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
[16]
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
[17]
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.
[18]
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
[19]
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.
[20]
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.
[21]
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.
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
[24]
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.
[25]
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.
[26]
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.
[27]
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.
[28]
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.
[29]
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.
