[1] LIU Y, GAYLE A A, WILDER-SMITH A, et al.The reproductive number of COVID-19 is higher compared to SARS coronavirus[J].Journal of Travel Medicine, 2020, 27(2):1-4. [2] REDMON J, FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2021-07-05].https://arxiv.org/abs/1804.02767. [3] 王艺皓, 丁洪伟, 李波, 等.复杂场景下基于改进YOLOv3的口罩佩戴检测算法[J].计算机工程, 2020, 46(11):12-22. WANG Y H, DING H W, LI B, et al.Mask wearing detection algorithm based on improved YOLOv3 in complex scenes[J].Computer Engineering, 2020, 46(11):12-22.(in Chinese) [4] WANG C Y, MARK LIAO H Y, WU Y H, et al.CSPNet:a new backbone that can enhance learning capability of CNN[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1571-1580. [5] HE K M, ZHANG X Y, REN S Q, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916. [6] LIU S, QI L, QIN H F, et al.Path aggregation network for instance segmentation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:8759-8768. [7] 叶子勋, 张红英.YOLOv4口罩检测算法的轻量化改进[J].计算机工程与应用, 2021, 57(17):157-168. YE Z X, ZHANG H Y.Lightweight improvement of YOLOv4 mask detection algorithm[J].Computer Engineering and Applications, 2021, 57(17):157-168.(in Chinese) [8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[EB/OL].[2021-07-05].https://arxiv.org/abs/2004.10934. [9] HOWARD A, SANDLER M, CHU G, et al.Searching for MobileNetv3[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2019:1314-1324. [10] 曹城硕, 袁杰.基于YOLO-Mask算法的口罩佩戴检测方法[J].激光与光电子学进展, 2021, 58(8):211-218. CAO C S, YUAN J.Mask-wearing detection method based on YOLO-Mask[J].Laser & Optoelectronics Progress, 2021, 58(8):211-218.(in Chinese) [11] LIN T Y, DOLLÁR P, GIRSHICK R, et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:936-944. [12] 余阿祥, 李承润, 于书仪, 等.多注意力机制的口罩检测网络[J].南京师范大学学报(工程技术版), 2021, 21(1):23-29. YU A X, LI C R, YU S Y, et al.Multi-attention mechanism of mask wearing detection network[J].Journal of Nanjing Normal University(Engineering and Technology Edition), 2021, 21(1):23-29.(in Chinese) [13] TAN M, PANG R, LE Q V, et al.EfficientDet:scalable and efficient object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:10781-10790. [14] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:7132-7141. [15] BODLA N, SINGH B, CHELLAPPA R, et al.Soft-NMS-improving object detection with one line of code[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:5562-5570. [16] 彭成, 张乔虹, 唐朝晖, 等.基于YOLOv5增强模型的口罩佩戴检测方法研究[J].计算机工程, 2022, 48(4):39-49. PENG C, ZHANG Q H, TANG Z H, et al.Research on mask wearing detection method based on YOLOv5 enhancement model[J].Computer Engineering, 2022, 48(4):39-49.(in Chinese) [17] HAN K, WANG Y H, TIAN Q, et al.GhostNet:more features from cheap operations[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1577-1586. [18] MA N N, ZHANG X Y, ZHENG H T, et al.ShuffleNet V2:practical guidelines for efficient CNN architecture design[EB/OL].[2021-07-05].https://arxiv.org/pdf/1807.11164.pdf. [19] 何涛, 俞舒曼, 徐鹤.基于条件生成对抗网络与知识蒸馏的单幅图像去雾方法[J].计算机工程, 2022, 48(4):165-172. HE T, YU S M, XU H.Single image dehazing method based on conditional generative adversarial network and knowledge distillation[J].Computer Engineering, 2022, 48(4):165-172.(in Chinese) [20] FAN S T, ZHANG X M, SONG Z H.Reinforced knowledge distillation:multi-class imbalanced classifier based on policy gradient reinforcement learning[J].Neurocomputing, 2021, 463:422-436. [21] 曹远杰, 高瑜翔.基于GhostNet残差结构的轻量化饮料识别网络[J].计算机工程, 2022, 48(3):310-314. CAO Y J, GAO Y X.Lightweight beverage recognition network based on GhostNet residual structure[J].Computer Engineering, 2022, 48(3):310-314.(in Chinese) [22] SANDLER M, HOWARD A, ZHU M L, et al.MobileNetV2:inverted residuals and linear bottlenecks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:4510-4520. [23] SUN K, XIAO B, LIU D, et al.Deep high-resolution representation learning for human pose estimation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:5686-5696. [24] REN S Q, HE K M, GIRSHICK R, et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149. [25] HOWARD A G, ZHU M L, CHEN B, et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2021-07-05].https://arxiv.org/abs/1704.04861. |