Face mask detection in real time using python
Suzon, Abdul Karim (2022)
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202204286306
https://urn.fi/URN:NBN:fi:amk-202204286306
Tiivistelmä
The aim of the thesis was to develop a Face Mask Detection system. For face mask identification, the thesis examines the use of Python programming with Deep Learning, TensorFlow, Keras, and OpenCV. The classifier uses the MobileNetV2 architecture as a foundation to do real-time mask detection. This system can be used in real-time applications which require face-mask detection for safety purpose due to the outbreak of coronavirus pandemic.
The system’s method is set up in such a way that it uses a video camera to capture people’s images and apply detecting algorithms. After the successful implementation of face mask detection with a video camera that helps in the detection of people wearing and not wearing a face mask. Using the visualisation algorithms, it is possible to show the detection percentage of calculation in various ways.
The study is divided into two sections including theoretical and practical sections. The theoretical part of the studies will cover the basics of python programming, deep learning, and convolutional neural network. The practical part will demonstrate how to develop an object detection model for real-time face mask identification using Python programming language and an object detection technique.
The system’s method is set up in such a way that it uses a video camera to capture people’s images and apply detecting algorithms. After the successful implementation of face mask detection with a video camera that helps in the detection of people wearing and not wearing a face mask. Using the visualisation algorithms, it is possible to show the detection percentage of calculation in various ways.
The study is divided into two sections including theoretical and practical sections. The theoretical part of the studies will cover the basics of python programming, deep learning, and convolutional neural network. The practical part will demonstrate how to develop an object detection model for real-time face mask identification using Python programming language and an object detection technique.