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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.

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• TITLE: Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method

  AUTHORS: Shaohan Wang, Xiangyu Wang, Xin Guo

  KEYWORDS: Face Mask Detection, Object Detection, Hybrid Dilation Convolution, Computer Vision

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.16 No.1, January 31, 2023

  ABSTRACT: 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%.