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

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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.