Three-Stage Recursive Learning Technique for Face Mask Detection on Imbalanced Datasets

doi:10.3390/math12193104

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/126937

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126937

Title:	Three-Stage Recursive Learning Technique for Face Mask Detection on Imbalanced Datasets
Authors:	Tsai, Chi-yi;Shih, Wei-hsuan
Keywords:	imbalanced data;recursive learning;face mask detection;object detection
Date:	2024-10-04
Issue Date:	2025-03-20 09:31:48 (UTC+8)
Publisher:	MDPI
Abstract:	In response to the COVID-19 pandemic, governments worldwide have implemented mandatory face mask regulations in crowded public spaces, making the development of automatic face mask detection systems critical. To achieve robust face mask detection performance, a high-quality and comprehensive face mask dataset is required. However, due to the difficulty in obtaining face samples with masks in the real-world, public face mask datasets are often imbalanced, leading to the data imbalance problem in model training and negatively impacting detection performance. To address this problem, this paper proposes a novel recursive model-training technique designed to improve detection accuracy on imbalanced datasets. The proposed method recursively splits and merges the dataset based on the attribute characteristics of different classes, enabling more balanced and effective model training. Our approach demonstrates that the carefully designed splitting and merging of datasets can significantly enhance model-training performance. This method was evaluated using two imbalanced datasets. The experimental results show that the proposed recursive learning technique achieves a percentage increase (PI) of 84.5% in mean average precision (mAP@0.5) on the Kaggle dataset and of 186.3% on the Eden dataset compared to traditional supervised learning. Additionally, when combined with existing oversampling techniques, the PI on the Kaggle dataset further increases to 88.9%, highlighting the potential of the proposed method for improving detection accuracy in highly imbalanced datasets.
Relation:	Mathematics 12(19),　3104
DOI:	10.3390/math12193104
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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