{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:23:38Z","timestamp":1786685018198,"version":"3.56.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"jimin yu","award":["61673079"],"award-info":[{"award-number":["61673079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.<\/jats:p>","DOI":"10.3390\/s21093263","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T02:54:58Z","timestamp":1620615298000},"page":"3263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":231,"title":["Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4"],"prefix":"10.3390","volume":"21","author":[{"given":"Jimin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University of Post and Telecommunications, Chongqing 400065, China"},{"name":"Key Lab of Industrial Wireless Networks and Networked Control of the Ministry of Education, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7662-300X","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Post and Telecommunications, Chongqing 400065, China"},{"name":"Key Lab of Industrial Wireless Networks and Networked Control of the Ministry of Education, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3389\/frym.2020.00074","article-title":"What Is COVID-19?","volume":"8","author":"Alberca","year":"2020","journal-title":"Front. Young Minds"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Advances in Kernel Methods. Support Vector Learn., 208.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"ref_4","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst., 25."},{"key":"ref_5","first-page":"315","article-title":"Deep Sparse Rectifier Neural Networks","volume":"15","author":"Glorot","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_6","first-page":"212","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","volume":"3","author":"Hinton","year":"2012","journal-title":"Comput. ENCE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_9","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R.B. (2015, January 11\u201318). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z. (November, January 27). Scale-Aware Trident Networks for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00615"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_18","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A. (2017). DSSD: Deconvolutional Single Shot Detector. arXiv."},{"key":"ref_19","unstructured":"Li, Z., and Zhou, F. (2017). FSSD: Feature Fusion Single Shot Multibox Detector. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jeong, J., Park, H., and Kwak, N. (2017). Enhancement of SSD by concatenating feature maps for object detection. arXiv.","DOI":"10.5244\/C.31.76"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Giger, M.L., and Suzuki, K. (2008). Computer-aided diagnosis. Biomedical Information Technology, Academic Press.","DOI":"10.1016\/B978-012373583-6.50020-7"},{"key":"ref_25","first-page":"427","article-title":"Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach. Computers","volume":"67","author":"Khan","year":"2021","journal-title":"Mater. Contin."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Buciu, I. (2020, January 5\u20136). Color quotient based mask detection. Proceedings of the 2020 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania.","DOI":"10.1109\/ISETC50328.2020.9301079"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102600","DOI":"10.1016\/j.scs.2020.102600","article-title":"Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection","volume":"65","author":"Loey","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_29","first-page":"108288","article-title":"A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic","volume":"167","author":"Ml","year":"2020","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102692","DOI":"10.1016\/j.scs.2020.102692","article-title":"SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2","volume":"66","author":"Nagrath","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Gool, L. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_33","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., and Zuo, W. (2020). Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Avenash, R., and Viswanath, P. (2019). Semantic Segmentation of Satellite Images using a Modified CNN with Hard-Swish Activation Function. VISIGRAPP.","DOI":"10.5220\/0007469604130420"},{"key":"ref_35","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2018). Searching for Activation Functions. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for MobileNetV3. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"Volume 29","author":"Keys","year":"1981","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Pro-Cessing"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 15\u201319). UnitBox: An Advanced Object Detection Network. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_39","unstructured":"Wang, Z.-Y., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., Yi, P., Jiang, K., Wang, N., and Pei, Y. (2020). Masked Face Recognition Dataset and Application. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cabani, A., Hammoudi, K., Benhabiles, H., and Melkemi, M. (2020). MaskedFace-Net-A Dataset of Correctly\/Incorrectly Masked Face Images in the Context of COVID-19. arXiv.","DOI":"10.1016\/j.smhl.2020.100144"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7722","DOI":"10.1109\/TII.2019.2954956","article-title":"Toward New Retail: A Benchmark Dataset for Smart Unmanned Vending Machines","volume":"16","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_42","unstructured":"Misra, D. (2019). Mish: A Self Regularized Non-Monotonic Neural Activation Function. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:58:17Z","timestamp":1760162297000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,8]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21093263"],"URL":"https:\/\/doi.org\/10.3390\/s21093263","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,8]]}}}