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Multi-Scenario Mask Detection Method and Implementation Based on Deep Learning

Mingwei Han

Abstract


Object detection is one of the core issues in the field of computer vision, with the task of accurately locating and recognizing all
objects of interest in images, determining the categories and positions of objects. Regarding masks as targets in object detection, utilizing deep
learning techniques to detect the wearing of masks on faces can greatly improve issues such as high manual supervision costs, low efficiency,
and subjective differences. In this paper, based on the YOLOV5 algorithm, we propose a mask detection technology that can accurately detect
the wearing of masks in real-time, and experimentally determine the occlusion boundaries.

Keywords


Deep learning; Target detection; Real-time detection of the YOLOv5 algorithm

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References


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