面向算力受限边缘环境的双分支多尺度感知人脸检测网络

doi:10.11959/j.issn.1000-436x.2020177

Abstract

Abstract:

Aiming at the problem that face detectors with complex deep neural structures are difficult to deploy in the resource-constrained edge computing environment,to reduce the resource consumption while maintain the accuracy in complex scenes such as multi-scale face changes,occlusion,blur,and illumination,SDPN(multi-scale aware dual path network) for face detection was proposed.The Face-ResNet (face residual neural network) was improved,and a dual path shallow feature extractor was used to understand the multi-scale information of the image through parallel branches.Then the deep and shallow feature fusion module,a combination of the underlying image information and the high-level semantic feature,was used in conjunction with the multi-scale awareness training strategy to supervise the multi-branch learning discriminating features.The experimental results show that SDPN can extract more diversified features,which effectively improve the accuracy and robustness of face detection while maintaining the efficiency of the model and low inference delay.

Key words: face detection, multi-scale aware, feature fusion, analysis of facial features, deep learning

CLC Number:

TP391.41

Qi QI,Yingxin MA,Jingyu WANG,Haifeng SUN,Jianxin LIAO. Multi-scale aware dual path network for face detection in resource-constrained edge computing environment[J]. Journal on Communications, 2020, 41(8): 165-174.

Figures/Tables 10

References 24

[1]	HU P , RAMANAN D . Finding tiny faces[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 951-959.
[2]	CHI C , ZHANG S , XING J ,et al. Selective refinement network for high performance face detection[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2019: 8231-8238.
[3]	LI J , WANG Y , WANG C ,et al. DSFD:dual shot face detector[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 5060-5069.
[4]	NAJIBI M , SAMANGOUEI P , CHELLAPPA R ,et al. SSH:single stage headless face detector[C]// Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 4875-4884.
[5]	TANG X , DU D K , HE Z ,et al. Pyramidbox:a contextassisted single shot face detector[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2018: 797-813.
[6]	HE Y , XU D , WU L ,et al. LFFd:a light and fast face detector for edge devices[J]. arXiv Preprint,arXiv:1904.10633, 2019
[7]	ZHANG S , ZHU X , LEI Z ,et al. Faceboxes:a CPU real-time face detector with high accuracy[C]// 2017 IEEE International Joint Conference on Biometrics. Piscataway:IEEE Press, 2017: 1-9.
[8]	YANG S , LUO P , LOY C C ,et al. Wider face:a face detection benchmark[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 5525-5533.
[9]	DENG J , DONG W , SOCHER R ,et al. ImageNet:a largescale hierarchical image database[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern. Piscataway:IEEE Press, 2009: 248-255.
[10]	REN S , HE K , GIRSHICK R ,et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]// Neural Information Processing Systems. Massachusetts:MIT Press, 2015: 91-99.
[11]	LIN T Y , DOLLAR P , GIRSHICK R ,et al. Feature pyramid networks for object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern. Piscataway:IEEE Press, 2017: 2117-2125.
[12]	HUANG L , YANG Y , DENG Y ,et al. Densebox:unifying landmark localization with end to end object detection[J]. arXiv Preprint,arXiv:1509.04874, 2015
[13]	ZHANG K , ZHANG Z , LI Z ,et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016,23(10): 1499-1503.
[14]	JIANG H , LEARNED-MILLER E , . Face detection with the faster R-CNN[C]// 2017 12th IEEE International Conference on Automatic Face ＆ Gesture Recognition. Piscataway:IEEE Press, 2017: 650-657.
[15]	LIU W , ANGUELOV D , ERHAN D ,et al. SSD:single shot multibox detector[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2016: 21-37.
[16]	REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 779-788.
[17]	ZHANG S , ZHU X , LEI Z ,et al. S3FD:single shot scaleinvariant face detector[C]// Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 192-201.
[18]	LAW H , DENG J . Cornernet:detecting objects as paired keypoints[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2018: 734-750.
[19]	DUAN , KAIWEN , ,et al. Centernet:keypoint triplets for object detection[C]// Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2019: 6569-6578.
[20]	LIU W , LIAO S , REN W ,et al. High-level semantic feature detection:a new perspective for pedestrian detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 5187-5196.
[21]	HE K , ZHANG X , REN S ,et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778.
[22]	ODENA A , DUMOULIN V , OLAH C . Deconvolution and checkerboard artifacts[J]. Distill, 2016,1(10):e3.
[23]	SINGH B , DAVIS L S . An analysis of scale invariance in object detection SNIP[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 3578-3587.
[24]	LIN T , GOYAL P , GIRSHICK R ,et al. Focal loss for dense object detection[C]// Proceedings of the IEEE International Conference on Computer vision. Piscataway:IEEE Press, 2017: 2980-2988.

Metrics

Recommended 0

No Suggested Reading articles found!

实验	下采样卷积核	浅层残差结构	DPE提取模块	深浅特征融合		检测精度
实验	下采样卷积核	浅层残差结构	DPE提取模块	深浅特征融合	简单	中等	困难
1	3×3	×	×	×	0.927	0.922	0.673
2	4×4	×	×	×	0.933	0.925	0.674
3	5×5	×	×	×	0.934	0.925	0.677
4	4×4	√	×	×	0.940	0.928	0.767
5	4×4	√	√	×	0.940	0.931	0.792
6	4×4	√	√	√	0.937	0.927	0.849

模型		检测精度		推理速度/(frame.s^-1)	模型参数量/MB
模型	简单	中等	困难	推理速度/(frame.s^-1)	模型参数量/MB
FTF^[1]	0.819	0.797	0.638	5.3	114.54
DSFD^[3]	0.949	0.936	0.845	1.7	457.99
PyramidBox^[5]	0.945	0.934	0.850	3.8	256.61
SSH^[4]	0.919	0.907	0.814	3.8	75.41
SDPN	0.937	0.927	0.849	6.7	65.06

模型		检测精度		时延/ms	速度/(frame.s^-1)
模型	简单	中等	困难	时延/ms	速度/(frame.s^-1)
SDPN	0.937	0.927	0.849	55.8	17.9
LFFD^[6]	0.896	0.865	0.770	31.27	31.98
FaceBoxes3.2x^[7]	0.791	0.794	0.715	25.37	39.41

Multi-scale aware dual path network for face detection in resource-constrained edge computing environment

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 24

Related Articles 15

Metrics

Recommended 0

[1]	Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG. Research on test strategy for randomness based on deep learning [J]. Journal on Communications, 2023, 44(6): 23-33.
[2]	Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver [J]. Journal on Communications, 2023, 44(6): 70-76.
[3]	Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI. Research on geomagnetic indoor high-precision positioning algorithm based on generative model [J]. Journal on Communications, 2023, 44(6): 211-222.
[4]	Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI. Lightweight decentralized learning-based automatic modulation classification method [J]. Journal on Communications, 2022, 43(7): 134-142.
[5]	Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI. Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF [J]. Journal on Communications, 2022, 43(6): 58-70.
[6]	Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment [J]. Journal on Communications, 2022, 43(5): 166-176.
[7]	Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI. Research progress of deep learning-based object detection of optical remote sensing image [J]. Journal on Communications, 2022, 43(5): 190-203.
[8]	Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation [J]. Journal on Communications, 2022, 43(4): 143-153.
[9]	Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems [J]. Journal on Communications, 2022, 43(12): 77-88.
[10]	Xueyuan DUAN, Yu FU, Kun WANG, Bin LI. LDoS attack detection method based on simple statistical features [J]. Journal on Communications, 2022, 43(11): 53-64.
[11]	Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG. Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture [J]. Journal on Communications, 2022, 43(11): 136-147.
[12]	Haiyan KANG, Yuanrui JI. Research on federated learning approach based on local differential privacy [J]. Journal on Communications, 2022, 43(10): 94-105.
[13]	Wei LIU, Cheng CHEN, Rui JIANG, Tao LU. Four-path unsupervised learning-based image defogging network [J]. Journal on Communications, 2022, 43(10): 210-222.
[14]	Hongxia ZHANG, Qi WANG, Dengyue WANG, Ben WANG. Honeypot contract detection of blockchain based on deep learning [J]. Journal on Communications, 2022, 43(1): 194-202.
[15]	Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG. Statistics release and privacy protection method of location big data based on deep learning [J]. Journal on Communications, 2022, 43(1): 203-216.