A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.
Khan, J.Y. and Alamin, M.A.A. (2021) A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection during COVID-19.
Lo, J.Y., Tsang, T.H., Leung, Y.-H., et al. (2005) Respiratory Infections during SARS Outbreak, Hong Kong, 2003. Emerging Infectious Diseases, 11, 1738. https://doi.org/10.3201/eid1111.050729
Cheng, V.C.-C., et al. (2020) 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, 81, 107-114. https://doi.org/10.1016/j.jinf.2020.04.024
Farfade, S.S., Saberian, M.J. and Li, L.-J. (2015) Multi-View Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, Shanghai, 23-26 June 2015, 643-650. https://doi.org/10.1145/2671188.2749408
Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B. and Alikhani, E. (2022) An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic. SN Computer Science, 3, 1-8. https://doi.org/10.1007/s42979-021-00894-0
Hendrycks, D., Mazeika, M., Kadavath, S. and Song, D. (2019) Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, 8-14 December 2019, 1-13.
Ge, S., Li, J., Ye, Q. and Luo, Z. (2017) Detecting Masked Faces in the Wild with LLE-CNNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21-26 July 2017, 2682-2690. https://doi.org/10.1109/CVPR.2017.53
Fan, X. and Jiang, M. (2021) RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, 17-20 October 2021, 832-837. https://doi.org/10.1109/SMC52423.2021.9659271
Nguyen, K.-D., Nguyen, H.H., Le, T.-N., et al. (2021) Effectiveness of Detection-Based and Regression-Based Approaches for Estimating Mask-Wearing Ratio. 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), Jodhpur, 15-18 December 2021, 1-8. https://doi.org/10.1109/FG52635.2021.9667046
Cabani, A., Hammoudi, K., Benhabiles, H. and Melkemi, M. (2021) MaskedFace-Net—A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19. Smart Health, 19, Article ID: 100144. https://doi.org/10.1016/j.smhl.2020.100144
https://github.com/AIZOOTech/FaceMaskDetection
Yang, S., Luo, P., Loy, C.-C. and Tang, X. (2016) Wider Face: A Face Detection Bench-Mark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 5525-5533. https://doi.org/10.1109/CVPR.2016.596
Kazemi, V. and Sullivan, J. (2014) One Millisecond Face Alignment with an Ensemble of Regression Trees. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 1867-1874. https://doi.org/10.1109/CVPR.2014.241
Wang, Z., et al. (2020) Masked Face Recognition Dataset and Application.
Huang, G.B., Mattar, M., Berg, T. and Learned-Miller, E. (2008) Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Workshop on Faces in “Real-Life” Images: Detection, Alignment, and Recognition, Marseille, August 2008.
Yi, D., Lei, Z., Liao, S. and Li, S.Z. (2014) Learning Face Representation from Scratch.
W. Xiangyu. https://github.com/shiningxy/RHF
Silveira, P., Teixeira, A. and Soares, C.G. (2013) Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. The Journal of Navigation, 66, 879-898. https://doi.org/10.1017/S0373463313000519
Ho, K.-F., Lin, L.-Y., Weng, S.-P. and Chuang, K.-J. (2020) Medical Mask versus Cotton Mask for Preventing Respiratory Droplet Transmission in Micro Environments. Science of the Total Environment, 735, Article ID: 139510. https://doi.org/10.1016/j.scitotenv.2020.139510
Gallo, O., Locatello, L.G., Mazzoni, A., Novelli, L. and Annunziato, F. (2021) The Central Role of the Nasal Microenvironment in the Transmission, Modulation, and Clinical Progression of SARS-CoV-2 Infection. Mucosal Immunology, 14, 305-316. https://doi.org/10.1038/s41385-020-00359-2
Perez, L. and Wang, J. (2017) The Effectiveness of Data Augmentation in Image Classification Using Deep Learning.
Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P. and Srinivas, A. (2020) Reinforcement Learning with Augmented Data. Advances in Neural Information Processing Systems, 33, 19884-19895.
Gedraite, E.S. and Hadad, M. (2011) Investigation on the Effect of a Gaussian Blur in Image Filtering and Segmentation. Proceedings ELMAR-2011, IEEE, Zadar, 14-16 September 2011, 393-396.
Shaked, D. and Tastl, I. (2005) Sharpness Measure: Towards Automatic Image Enhancement. IEEE International Conference on Image Processing, Vol. 1, I-937. https://doi.org/10.1109/ICIP.2005.1529906
Kwon, O.-Y. and Chien, S.-I. (2011) Improved Posterized Color Images Based on Color Quantization and Contrast Enhancement. Proceedings International Conference Machine Vision, Image Processing, and Pattern Analysis, Vol. 5, 1203-1206.
Soroush, M., Wessel-Berg, D., Torsaeter, O. and Kleppe, J. (2014) Investigating Residual Trapping in CO2 Storage in Saline Aquifers—Application of a 2D Glass Model, and Image Analysis. Energy Science & Engineering, 2, 149-163. https://doi.org/10.1002/ese3.32
Wang, P., et al. (2018) Understanding Convolution for Semantic Segmentation. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, 12-15 March 2018, 1451-1460. https://doi.org/10.1109/WACV.2018.00163
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2017) DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848. https://doi.org/10.1109/TPAMI.2017.2699184
Fan, X., Jiang, M. and Yan, H. (2021) A Deep Learning Based Light-Weight Face Mask Detector with Residual Context Attention and Gaussian Heatmap to Fight against COVID-19. IEEE Access, 9, 96964-96974. https://doi.org/10.1109/ACCESS.2021.3095191
Yu, F. and Koltun, V. (2015) Multi-Scale Context Aggregation by Dilated Convolutions.
Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 580-587. https://doi.org/10.1109/CVPR.2014.81
Wang, S., Zargar, S.A. and Yuan, F.-G. (2021) Augmented Reality for Enhanced Visual Inspection through Knowledge-Based Deep Learning. Structural Health Monitoring, 20, 426-442. https://doi.org/10.1177/1475921720976986
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778. https://doi.org/10.1109/CVPR.2016.90
Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition.
Sutskever, I., Martens, J., Dahl, G. and Hinton, G. (2013) On the Importance of Initialization and Momentum in Deep Learning. International Conference on Machine Learning, PMLR, Atlanta, 17-19 June 2013, 1139-1147.
Paszke, A., et al. (2019) An Imperative Style, High-Performance Deep Learning Library. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, 8-14 December 2019, 8026.
Li, Y., Yang, M., Peng, D., Li, T., Huang, J. and Peng, X. (2022) Twin Contrastive Learning for Online Clustering. International Journal of Computer Vision, 130, 2205-2221. https://doi.org/10.1007/s11263-022-01639-z
Peng, X., Li, Y., Tsang, I.W., Zhu, H., Lv, J. and Zhou, J.T. (2022) XAI Beyond Classification: Interpretable Neural Clustering. Journal of Machine Learning Research, 23, 6:1-6:28.
Li, Y., Hu, P., Liu, Z., Peng, D., Zhou, J.T. and Peng, X. (2021) Contrastive Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 8547-8555. https://doi.org/10.1609/aaai.v35i10.17037