Application of Yolo on Mask Detection Task

TL;DR

This paper introduces a faster mask detection method using YOLO instead of Mask-RCNN, combined with few-shot learning techniques to handle dataset imbalance, aiming for real-time accuracy and efficiency.

Contribution

It presents a novel combination of YOLO and few-shot learning to improve real-time mask detection performance over existing methods.

Findings

YOLO-based model achieves higher processing speed.

Few-shot learning improves detection accuracy on imbalanced datasets.

Method maintains accuracy while increasing efficiency.

Abstract

2020 has been a year marked by the COVID-19 pandemic. This event has caused disruptions to many aspects of normal life. An important aspect in reducing the impact of the pandemic is to control its spread. Studies have shown that one effective method in reducing the transmission of COVID-19 is to wear masks. Strict mask-wearing policies have been met with not only public sensation but also practical difficulty. We cannot hope to manually check if everyone on a street is wearing a mask properly. Existing technology to help automate mask checking uses deep learning models on real-time surveillance camera footages. The current dominant method to perform real-time mask detection uses Mask-RCNN with ResNet as the backbone. While giving good detection results, this method is computationally intensive and its efficiency in real-time face mask detection is not ideal. Our research proposes a new approach to mask detection by replacing Mask-R-CNN with a more efficient model "YOLO" to increase the processing speed of real-time mask detection and not compromise on accuracy. Besides, given the small volume as well as extreme imbalance of the mask detection datasets, we adopt a latest progress made in few-shot visual classification, simple CNAPs, to improve the classification performance.