Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method
Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method作者机构：Shanghai Ship and Shipping Research Institute Shanghai China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai China COSCO SHIPPING Technology Co. Ltd. Shanghai China Marine Traffic Safety and Application Laboratory College of Merchant Marine Shanghai Maritime University Shanghai China
出 版 物：《Journal of Software Engineering and Applications》 (软件工程与应用（英文）)
年 卷 期：2023年第16卷第1期
页 面：1-19页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Face Mask Detection Object Detection Hybrid Dilation Convolution Computer Vision
摘 要：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%.
维普期刊数据库