Lightweight Mask-Wearing Detection Algorithm with Multi-Scale Feature Fusion

ABSTRACT

Standardized usage of face masks is effective as a non-pharmaceutical intervention to prevent the spread of infectious respiratory diseases，such as COVID-19 and influenza. In the current epidemic situation，wearing face masks correctly is especially important. Most existing mask-wearing detection algorithms involve problems such as complex structures，high training difficulty，and insufficient feature extraction. Therefore，this study proposes a lightweight mask-wearing detection algorithm based on multi-scale feature fusion and the YOLOv4-Tiny network，called L-MFFN-YOLO. L-MFFN-YOLO improves on the original residual structure and uses a lightweight residual module to promote rapid convergence. Moreover，it reduces the computational load while ensuring detection accuracy. Based on the original network’s 13×13 and 26×26 feature maps，52×52 feature branches are added to enhance the ability of the lower feature layer to express information and reduce the false negative rate for small targets.On this basis，a Multi-level Cross Fusion （MCF） structure is used to maximally extract useful information so as to improve feature utilization. In addition to detecting mask-wearing，a category of masks worn incorrectly is added to the dataset and manually labeled. The www.eciexperimental results show that the size of the proposed L-MFFN-YOLO model is only 5.8 MB，which is 76% smaller than that of the original YOLOv4-Tiny. Moreover，the mean Average Precision（mAP）of the proposed approach is 5.25 percentage points higher，and its processing time is 14 ms faster on an equivalent CPU.These results demonstrate that the proposed approach can meet the requirements of accuracy and real-time operation in resource-constrained devices to detect faces wearing masks. © 2022, Editorial Office of Computer Engineering. All rights reserved.