开源人脸口罩检测模型和数据 Detect faces and determine whether people are wearing mask.

In this project, we develop a pipeline to detect unmasked faces in images. This can, for example, be used to alert people that do not wear a mask when entering a building.

用opencv的dnn模块实现人脸口罩检测，包含C++和Python两种版本的实现

To further help combat the corona virus (COVID-19), this repository can be used for real-time face mask detection using webcam with an impressive accuracy. For convenience, the source codes are shared both in MATLAB and Python.

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FaceMaskDetection Implementation with c++

using opencv to translate the code from https://github.com/AIZOOTech/FaceMaskDetection

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COVIDSAVIOUR: A Novel Sensor-Fusion and Deep Learning-Based Framework for Virus Outbreaks

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Neural Network Algorithm to detect people wearing face masks and take actions accordingly

本项目是一个基于 YOLOv8 的口罩检测系统，支持检测三种状态：正确佩戴口罩、未佩戴口罩、口罩佩戴不规范。本系统提供完整的训练流程和灵活的预测接口，适用于实时视频流、图片文件、摄像头输入等多种场景。旨在通过深度学习技术实现对图像和视频中人物是否正确佩戴口罩的高效、准确检测。可用于公共场所监控、门禁系统等场景，助力疫情防控与公共卫生管理。

Face mask detection is a deep learning task that involves developing models to identify whether a person in an image or video is wearing a face mask or not.

Determines whether a specific user is wearing a face mask or not using a 2-factor approach written in Python with TensorFlow and OpenCV modules. Includes a Google Colab notebook tutorial and code for real-time detection using OpenCV.

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face mask detection, including face detection and mask recognition

Face mask detection TinyYolo2 Dl4j

This repo contains the additional files required to run YOLOv5 detections on Raspberry Pi boards. Copy these files into /yolov5 folder

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Training models for Face Mask and Detecting face mask of a face

To develop a “Face mask detection” software using Python Programming Language & DataScience, to facilitate easy mask detection from real time video stream as well as from image. About Module: The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision, data science and deep learning.

Last year in Dec 2020, I built a Face Mask Detection System using Python and OpenCV.

Simple Flask based Web Page that consist Face Mask detection Module for Web-Cam

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Deep Learning in Browser

COVID-19 pandemic has tremendously affected our day-to-day life affecting world trade and movements. Wearing a protective face mask has become mandatory. In the near future, many public service providers will ask the customers to wear masks to avail of their services. Therefore, face mask detection has become an essential task to help global society. This paper presents a simplified approach to achieve this purpose using some basic deep Learning packages like TensorFlow, Keras, and OpenCV. The proposed methodology detects the face from the image/video stream correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method obtains accuracy up to 95.55% and 94.23% respectively on two different datasets. We explore optimized values of parameters using the Convolutional Neural Network model to detect the presence of masks correctly without causing over-fitting.

Face Mask Detection with ResNets trained with 3 labels: with_mask, without_mask, mask_weared_incorrect.

An AI model to detect if people are following social distancing & wearing masks for safety.

PBL project for 2nd year exam.