{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:15:23Z","timestamp":1770225323910,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"North China University of Water Resources and Electric Power","award":["YK-2021-93"],"award-info":[{"award-number":["YK-2021-93"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%\u2014an improvement of 2.1% compared to the baseline model (YOLOv5).<\/jats:p>","DOI":"10.3390\/s22134933","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T22:43:28Z","timestamp":1656542608000},"page":"4933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5"],"prefix":"10.3390","volume":"22","author":[{"given":"Shuyi","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1298-6922","authenticated-orcid":false,"given":"Lulu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China"}]},{"given":"Tianyou","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China"}]},{"given":"Yunyu","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8652-3712","authenticated-orcid":false,"given":"Yinlei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TITS.2017.2784093","article-title":"A new CNN-based method for multi-directional car license plate detection","volume":"19","author":"Xie","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1109\/TITS.2018.2847291","article-title":"Toward end-to-end car license plate detection and recognition with deep neural networks","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","first-page":"23","article-title":"CCTSDB 2021: A More Comprehensive Traffic Sign Detection Benchmark","volume":"12","author":"Zhang","year":"2022","journal-title":"Hum.-Cent. Comput. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qin, B., and Li, D. (2020). Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors, 20.","DOI":"10.21203\/rs.3.rs-28668\/v1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kortli, Y., Jridi, M., Al Falou, A., and Atri, M. (2020). Face recognition systems: A survey. Sensors, 20.","DOI":"10.3390\/s20020342"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, M., Awasthi, N., Rad, N.M., Pluim, J.P., and Lopata, R.G. (2021). Advanced Ultrasound and Photoacoustic Imaging in Cardiology. Sensors, 21.","DOI":"10.3390\/s21237947"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hossain, S., and Lee, D.-j. (2019). Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors, 19.","DOI":"10.3390\/s19153371"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xia, R., Chen, Y., and Ren, B. (2022). Improved anti-occlusion object tracking algorithm using Unscented Rauch-Tung-Striebel smoother and kernel correlation filter. J. King Saud Univ.-Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2022.02.004"},{"key":"ref_9","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 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_10","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_14","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_17","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_18","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). 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_20","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). Dssd: Deconvolutional single shot detector. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_23","first-page":"12","article-title":"Mask wearing detection algorithm based on improved YOLOv3 in complex scenes","volume":"46","author":"Yihao","year":"2020","journal-title":"Comput. Eng."},{"key":"ref_24","first-page":"7","article-title":"Detection of Mask Wearing in Dim Light Based on Attention Mechanism","volume":"51","author":"Lei","year":"2022","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104341","DOI":"10.1016\/j.imavis.2021.104341","article-title":"FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public","volume":"117","author":"Wu","year":"2022","journal-title":"Image Vis. Comput."},{"key":"ref_26","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":"2021","journal-title":"Sustain. Cities"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, R., Li, J., and Fei, L. (2020). Face Detection Based on YOLOv3. Recent Trends in Intelligent Computing, Communication and Devices, Springer.","DOI":"10.1007\/978-981-13-9406-5_34"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nieto-Rodriguez, A., Mucientes, M., and Brea, V.M. (2015, January 17\u201319). System for Medical Mask Detection in the Operating Room Through Facial Attributes. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-319-19390-8_16"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_31","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_32","unstructured":"Wang, Z., 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. Computer Vision and Pattern Recognition. arXiv."},{"key":"ref_33","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 IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4933\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:38Z","timestamp":1760139638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":33,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134933"],"URL":"https:\/\/doi.org\/10.3390\/s22134933","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}