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Jones, \u201cRapid object detection using a boosted cascade of simple features,\u201d CVPR'1, vol.1, pp.I-I, 2001. 10.1109\/cvpr.2001.990517","DOI":"10.1109\/CVPR.2001.990517"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] R. Lienhart and J. Maydt, \u201cAn extended set of haar-like features for rapid object detection,\u201d Proceedings international conference on image processing, vol.1, pp.I-I, 2002. 10.1109\/icip.2002.1038171","DOI":"10.1109\/ICIP.2002.1038171"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \u201cRich feature hierarchies for accurate object detection and semantic segmentation,\u201d CVPR'14, pp.580-587, 2014. 10.1109\/cvpr.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] R. Girshick, \u201cFast r-cnn,\u201d CVPR'15, pp.1440-1448, 2015. 10.1109\/iccv.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] K. He, X. Zhang, S. Ren, and J. Sun, \u201cSpatial pyramid pooling in deep convolutional networks for visual recognition,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.9, pp.1904-1916, 2015. 10.1109\/tpami.2015.2389824","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Ren, K. He, R. Girshick, and J. Sun, \u201cFaster r-cnn: Towards real-time object detection with region proposal networks,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.6, pp.1137-1149, 2017. 10.1109\/tpami.2016.2577031","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \u201cYou Only Look Once: Unified, Real-Time Object Detection,\u201d CVPR'16, pp.779-788, 2016. 10.1109\/cvpr.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Redmon and A. Farhadi, \u201cYOLO9000: Better, Faster, Stronger,\u201d CVPR'17, pp.6517-6525, 2017. 10.1109\/cvpr.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"12","unstructured":"[12] J. Redmon and A. Farhadi, \u201cYolov3: An incremental improvement,\u201d arXiv preprint arXiv:1804.02767, 2018."},{"key":"13","unstructured":"[13] A. Bochkovskiy, C.Y. Wang, and H.Y.M. Liao, \u201cYolov4: Optimal speed and accuracy of object detection,\u201d arXiv preprint arXiv:2004.10934, 2020."},{"key":"14","unstructured":"[14] C. Li, L. Li, H. Jiang, K. Weng, and Y. Geng, \u201cYOLOv6: A single-stage object detection framework for industrial applications,\u201d arXiv preprint arXiv:2209.02976, 2022."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C.-Y. 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Varma, \u201cA novel algorithm for mask detection and recognizing actions of human,\u201d Expert Systems with Applications, vol.198, p.116823, 2022. 10.1016\/j.eswa.2022.116823","DOI":"10.1016\/j.eswa.2022.116823"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] B.S. Bayu Dewantara and D. Twinda Rhamadhaningrum, \u201cDetecting multi-pose masked face using adaptive boosting and cascade classifier,\u201d IES'6, pp.436-441, 2020. 10.1109\/ies50839.2020.9231934","DOI":"10.1109\/IES50839.2020.9231934"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] G. Zhao, S. Zou, and H. Wu, \u201cImproved Algorithm for Face Mask Detection Based on YOLO-v4,\u201d International Journal of Computational Intelligence Systems, vol.16, no.1, p.104, 2023. 10.1007\/s44196-023-00286-7","DOI":"10.1007\/s44196-023-00286-7"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] Z. Han, H. Huang, Q. Fan, Y. Li, and Y. Li, \u201cSMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic,\u201d Computer methods and programs in biomedicine, vol.221, p.106888, 2022. 10.1016\/j.cmpb.2022.106888","DOI":"10.1016\/j.cmpb.2022.106888"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] H. Xiao, B. Wang, J. Zheng, L. Liu, and C.L.P. Chen, \u201cA Fine-grained Detector of Face Mask Wearing Status Based on Improved YOLOX,\u201d IEEE Transactions on Artificial Intelligence, pp.1-15, 2023. 10.1109\/tai.2023.3300668","DOI":"10.1109\/TAI.2023.3300668"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] F. Yu, G. Zhang, F. Zhao, X. Wang, H. Liu, P. Lin, and Y. 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Shi, \u201cFace Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism,\u201d Computational Intelligence and Neuroscience, vol.2022, pp.1-10, 2022. 10.1155\/2022\/2452291","DOI":"10.1155\/2022\/2452291"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] C. Wang, B. Zhang, Y. Cao, M. Sun, K. He, Z. Cao, and M. Wang, \u201cMask Detection Method Based on YOLO-GBC Network,\u201d Electronics, vol.12, no.2, p.408, 2023. 10.3390\/electronics12020408","DOI":"10.3390\/electronics12020408"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] J. Wang, J. Wang, X. Zhang, and N. Yu, \u201cA Mask-Wearing Detection Model in Complex Scenarios Based on YOLOv7-CPCSDSA,\u201d Electronics, vol.12, no.14, p.3128, 2023. 10.3390\/electronics12143128","DOI":"10.3390\/electronics12143128"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, \u201cPath aggregation network for instance segmentation,\u201d CVPR'18, pp.8759-8768, 2018. 10.1109\/cvpr.2018.00913","DOI":"10.1109\/CVPR.2018.00913"},{"key":"30","unstructured":"[30] C.Y. Wang, I.H. Yeh, and H.Y.M. Liao, \u201cYou only learn one representation: Unified network for multiple tasks,\u201d arXiv preprint arXiv:2105.04206, 2021."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] M.G.D. Nascimento, V. Prisacariu, and R. Fawcett, \u201cDsconv: Efficient convolution operator,\u201d ICCV'19, pp.5148-5157, 2019. 10.1109\/iccv.2019.00525","DOI":"10.1109\/ICCV.2019.00525"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, and W. Zuo, \u201cEnhancing geometric factors in model learning and inference for object detection and instance segmentation,\u201d IEEE Trans. Cybern., vol.52, no.8, pp.8574-8586, 2021. 10.1109\/tcyb.2021.3095305","DOI":"10.1109\/TCYB.2021.3095305"},{"key":"33","unstructured":"[33] M. Siliang and X. Yong, \u201cMPDIoU: A loss for efficient and accurate bounding box regression,\u201d arXiv preprint arXiv:2307.07662, 2023."},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, \u201cGeneralized intersection over union: A metric and a loss for bounding box regression,\u201d CVPR'19, pp.658-666, 2019. 10.1109\/cvpr.2019.00075","DOI":"10.1109\/CVPR.2019.00075"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] Z., Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, \u201cDistance-IoU loss: Faster and better learning for bounding box regression,\u201d Proceedings of the AAAI conference on artificial intelligence, vol.34, no.7, pp.12993-13000, 2020. 10.1609\/aaai.v34i07.6999","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] Y.-F. Zhang, W. Ren, Z. Zhang, Z. Jia, L. Wang, and T. Tan, \u201cFocal and efficient IOU loss for accurate bounding box regression,\u201d Neurocomputing, vol.506, pp.146-157, 2022. 10.1016\/j.neucom.2022.07.042","DOI":"10.1016\/j.neucom.2022.07.042"},{"key":"37","unstructured":"[37] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, and W. Wang, \u201cMobilenets: Efficient convolutional neural networks for mobile vision applications,\u201d arXiv preprint arXiv:1704.04861, 2017."},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] X. Zhang, X. Zhou, M. Lin, and J. Sun, \u201cShufflenet: An extremely efficient convolutional neural network for mobile devices,\u201d CVPR'18, pp.6848-6856, 2018. 10.1109\/cvpr.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, \u201cGhostNet: More Features from Cheap Operations,\u201d CVPR'20, pp.1580-1589, 2020. 10.1109\/cvpr42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"40","unstructured":"[40] H. Li, J. Li, H. Wei, Z. Liu, and Z. 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