Abstract
The computer vision which is an important aspect of Artificial Intelligence. The object detection is the most researchable area with deep learning algorithms. Now in the current COVID – 19 pandemics, the social distancing is a mandatory factor to prevent this transmission of this deadly virus. The government is struggling to handle the persons without wearing masks in public places. Our work concentrates on the object detection of face masks using the state-of-the-art methodologies like YOLO, SSD, RCNN, Fast RCNN and Faster RCNN with different backbone architectures like ResNet, MobileNet, etc. This paper brings out various ensemble methods by combining the state of art methodologies and compare those combinations to identify the best performance, in choice of the dataset of the application. We have obtained the highest performance benchmark with the usage of Faster RCNN – ResNet50 among the other ensemble methods. All the performance evaluation metrics are compared with one other with the same face mask detection image dataset. In this paper, we present a balancing collation of the ensemble methods of object detection algorithms.
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References
Ahmed, I., Ahmad, M., Rodrigues, J.J.P.C., Jeon, G., Din, S.: A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65(2021), 102571 (2021). https://doi.org/10.1016/j.scs.2020.102571
Jia, Y., et al.: Caffe: Convolutional Architecture for Fast Feature Embedding (2014)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (2017). https://doi.org/10.1109/CVPR.2017.690
Shinde, S., Kothari, A., Gupta, V.: YOLO based human action recognition and localization. Procedia Comput. Sci. 133, 831–838 (2018). https://doi.org/10.1016/j.procs.2018.07.112
Dong, E., Zhu, Y., Ji, Y., Du, S.: An improved convolution neural network for object detection using YOLOv2. In: Proceedings of 2018. IEEE (2018)
Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.): ECCV 2016. LNCS, vol. 9908. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0
Ejaz, S., Islam, R., Sifatullah, Md., Sarker, A.: Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 (ICASERT 2019) (2019)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2014). https://doi.org/10.1109/CVPR.2014.276
Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8) (2014)
Girshick, R., Donahue, J., Darrell, T., Malik, J.:Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014). https://doi.org/10.1109/CVPR.2014.81
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016). https://doi.org/10.1109/CVPR.2016.91
Kumar, K.S., Semwal, V.J., Tripathi, R.C.: Real time face recognition using adaboost improved fast PCA algorithm (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016). https://doi.org/10.1109/CVPR.2016.90
Roh, M.-C., Lee, J.-Y.: Refining faster-RCNN for accurate object detection. In: 15th IAPR International Conference on Machine Vision Applications (MVA). Nagoya University, Nagoya, Japan (2017)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2016). https://doi.org/10.1109/TPAMI.2015.2437384
Nguyen, N.-D., Do, T., Ngo, T.D., Le, D.-D.: An evaluation of deep learning methods for small object detection. J. Electr. Comput. Eng. 2020, 1–18 (2020). https://doi.org/10.1155/2020/3189691
Wu, X., Sahoo, D., Hoi, S.C.H.: Recent advances in deep learning for object detection. Neurocomputing 396(2020), 39–64 (2020). https://doi.org/10.1016/j.neucom.2020.01.085
Zhan, S., Tao, Q.-Q., Li, X.-H.: Face Detection using representation learning. J. Neurocomput. 187(2016), 19–26 (2016). https://doi.org/10.1016/j.neucom.2015.07.130
Loey, M., Manogaran, G., Mohamed, H.N., Taha, N.E., Khalifa, M.: A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. J. Int. Meas. Confed. (2021). https://doi.org/10.1016/j.measurement.2020.108288
Din, N.U., Javed, K., Bae, S., Yi, J.: A novel GAN-based network for unmasking of masked face. IEEE Access 8, 44276–44287 (2020)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Loey, M., Manogaran, G., Tahad, M.H.N., Khalifa, N.E.M.: Fighting against COVID-19: a novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection (2021)
Nguyen, H.: Fast object detection framework based on mobilenet V2 architecture and enhanced feature pyramid. J. Theor. Appl. Inf. Technol. 98,(05) (2020). ISSN: 1992-8645
Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition. IEEE Computer Society (2017). https://doi.org/10.1109/FG.2017.82
Padilla, R., Netto, S.L., da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms. In: Proceedings of the IWSSIP 2020. IEEE Explore (2020)
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DhivyaShree, M., Sarumathi, K.R., Durai, R.S.V. (2022). An Ensemble Model for Face Mask Detection Using Faster RCNN with ResNet50. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_48
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