LeNet Convolutional Neural Network for Face Mask Usage Classification Using a Low-Cost Device
Background: One of the efforts to prevent the spread of the COVID-19 virus is to wear a face mask in public places.However, there are still many people who use masks in the wrong way, and some do not even wear masks when in public places. From these problems, we need an image-based classification system that can be employed to identify the use of face masks. The system built must use a low-cost device to be purchased by sundry groups. In previous studies, some classification systems for face mask use were designed using various methods, but there were limitations. The Convolutional Neural Network (CNN) method provides high accuracy. However, it has a heavy computational level and cannot be used in real-time on low-cost devices. In contrast, the haar-cascade method provides a fast processing time but is less accurate than the CNN method.
Objective: In this article, research was conducted on the development of image processing algorithms for the classification process of face mask use using low-cost devices.
Methods: The method used was CNN with LeNet architecture which has a light computational level. In the machine learning process, a dataset of 400 images was used, which was split into 240 images for training needs and 160 images for validation needs.
Result: This study produced a classification with an accuracy rate of 98.75%. The prediction process that is carried out using a low-cost device requires an average time of 0.235 seconds.
Conclusion: This research showed that the system can be run in real time
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