This project explains a method of building a Face Mask Detector using Convolutional Neural Networks (CNN) Python, Keras, Tensorflow, and OpenCV. With further improvements, these types of models could be integrated with CCTV or other types of cameras to detect and identify people without masks. With the prevailing worldwide situation due to the COVID-19 pandemic, these types of systems would be very supportive for many kinds of institutions around the world.

1. Face Mask DetectionusingConvolutional Neural Networks
Thisprojectexplainsamethodof buildingaFace Mask DetectorusingConvolutionalNeural
Networks(CNN) Python,Keras,Tensorflow,andOpenCV.Withfurtherimprovements,thesetypesof
modelscouldbe integratedwithCCTV orothertypesof camerasto detectandidentifypeople
withoutmasks.Withthe prevailingworldwide situationdue tothe COVID-19pandemic,thesetypes
of systemswouldbe verysupportive formanykindsof institutionsaround the world.
In thisPythonprogrammingvideo,we willlearnbuildingaFace Mask DetectorusingKeras,
Tensorflow, MobileNet,andOpenCV.We willalsosee how toapplythistoa Live VideoCamera.
Withfurtherimprovements,thesetypesof modelscouldbe integratedwithCCTV camerastodetect
and identifypeople withoutmasks.
The face maskdetectordidn'tuse any morphedmaskedimagesdataset.The model isaccurate,and
since the MobileNetV2architecture isused,it'salsocomputationallyefficientandthusmakingit
easiertodeploythe model toembeddedsystems(RaspberryPi,Google Coral,etc.).Thissystemcan,
therefore,be usedinreal-timeapplicationsthatrequireface-maskdetectionforsafetypurposesdue
to the outbreakof Covid-19.Thisprojectcan be integratedwithembeddedsystemsforapplication
inairports,railwaystations,offices,schools,andpublicplacestoensure thatpublicsafetyguidelines
are followed.
Introduction:

2. In thismodule,we'lldiscussourface maskdetector,detailinghow ourcomputervision/deep
learningpipeline will be implemented.Fromthere,we'llreview the datasetwe'llbe usingtotrain
our customface maskdetector.
I'll thenshowyouhow to implementaPythonscripttotrain a face mask detectoronour dataset
usingKerasand TensorFlow.We’ll use thisPythonscripttotraina face maskdetectorand review
the results.
Project Directory:
We'll be reviewing2Pythonscriptsinthisproject:
1. train_mask_detector.py: Acceptsour inputdatasetandfine-tunesMobileNetV2uponitto
create our mask_detector.model.A traininghistoryplot.pngcontainingaccuracy/losscurves
isalso produced.
2. detect_mask_video.py:Usingyourwebcam, thisscriptappliesface maskdetectiontoevery
frame inthe stream.
Mobile Nets:
Nowthat we've reviewedourface maskdatasetanddone withthe data-preprocessing.Let’slearn
howwe can use Kerasand TensorFlowtotraina classifierto automatically detectwhetheraperson
iswearinga maskor not.
To accomplishthistask,we'll be fine-tuningthe MobileNetV2architecture,ahighlyefficient
architecture thatcan be appliedtoembeddeddeviceswithlimitedcomputational capacity.(ex.,
RaspberryPi,Google Coral,NVIDIA JetsonNano,etc.)
Deployingourface maskdetectortoembeddeddevices couldreduce the costof manufacturingsuch
face mask detectionsystems,hence whywe choose touse thisarchitecture.
We will bypassthe traditionalconvolutionmethodof image processinginneural networksand
introduce "MobileNet".
MobileNetisastreamlinedarchitecture that usesdepthwise separable convolutionstoconstruct
lightweightdeepconvolutional neural networksandprovidesanefficientmodel formobile and
embeddedvisionapplications.The structure of MobileNetisbasedondepthwise separablefilters.
MobileNetsare veryfastercomparedtothe convolutionNeural Network.AndalsoMobileNetsuses
fewerparameters.One disadvantagewithMobilenetsisthatthe outputwill be lessaccurate
comparedto itscompetitors.
By doingthisDIY project, youlearnedhow tocreate a Face Mask DetectorusingPython,Keras,
OpenCV,andMobileNet.
To create our face maskdetector,we trainedatwo-classmodel of peoplewearingmasks and
people notwearingmasks.
We fine-tunedMobileNetV2onourmask/nomask datasetand obtainedaclassifierthatis ~99%
accurate.

3. The projectcan be explainedclearlyinthe below steps.
1. Introduction
2. DataSet
3. ProjectDirectory
4. Data Preprocessing
5. MobileNets
6. Trainingthe Model
7. Run the Model
8. Applythe model incamera
9. Result
10. Summary
11. Resources
12. Module Test
SkillPractical Artificial Intelligence Learningpath give astructure to trainingprograms. Thislearning
path isdesignedforindividualswhowanttolearnhow to use the mostcommontopicsin Artificial
Intelligence.Theywillcoverinastepby stepprocedure frombeginnertoadvanced.