Xuanyu Liu
ABSTRACT
In this paper, a lightweight network based on improved YOLOv4 is proposed to solve the problems of complex model structure and unsatisfactory performance of detection speed in fast-moving situations. Firstly, the inverted residual blocks (IRB) based on Mobilenetv2 are adopted into the backbone feature extraction network to reduce the complexity of the model structure. Then, the feature fusion network based on the depth-wise separable convolution is applied to minimize model calculations and parameters. Experimental results show that the proposed model has the advantages of high accuracy, fast detection speed, and lightweight, which has satisfied the requirements of real-time detection of mask-wearing in fast-moving situations.
- CHENG V C C, WONG S C, CHUANG V W M, The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. Journal of Infection, 2020, 81(1), 107-114.Google Scholar
Cross Ref
- GIRSHICK R, DONAHUE J, DARRELL T, Rich feature hierarchies for accurate object detection and semantic segmentatio. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, 580-587.Google ScholarDigital Library
- GIRSHICK R. Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, 2015, 1440-1448.Google ScholarDigital Library
- REN S, HE K, GIRSHICK R, Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6), 1137-1149.Google ScholarDigital Library
- HE K, GKIOXARI G, DOLLÁR P, Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision. 2017, 2961-2969.Google Scholar
- HE K, ZHANG X, REN S, Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9), 1904-1916.Google Scholar
- REDMON J, DIVVALA S, GIRSHICK R, You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 779-788.Google ScholarCross Ref
- LIU W, ANGUELOV D, ERHAN D, SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, 2016, 21-37.Google ScholarCross Ref
- CHAVDA A, DSOUZA J, BADGUJAR S, Multi-stage CNN architecture for face mask detection. Proceedings of the IEEE International Conference for Convergence in Technology, 2021, 1-8.Google Scholar
- GUPTA P, SHARMA V, VARMA S. A novel algorithm for mask detection and recognizing actions of human. Expert Systems with Applications, 2022, 198, 1-10.Google Scholar
- WU P, LI H, ZENG N, FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public. Image and Vision Computing, 2022, 117, 104341.Google ScholarDigital Library
- NAGRATH P, JAIN R, MADAN A, SSDMNV2: A real-time DNN-based face mask detection system using single shot multibox detector and MobileNetV2 [J]. Sustainable cities and society, 2021, 66, 102692.Google Scholar
- BOCHKOVSKIV A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection. ArXiv Preprint, arXiv:2004.10934, 2020.Google Scholar
- SANDLER M, HOWARD A, ZHU M, Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 4510-4520.Google ScholarCross Ref
- HOWARD A G, ZHU M, CHEN B, Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv Preprint, ArXiv:1704.04861, 2017.Google Scholar
Index Terms
- A Method of Mask-Wearing Detection Based on Improved YOLOv4
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