Face Mask Detection using Single Shot Multibox Detector and Mobile Net
P. Ranjana, K. Ramesh, Shivanandhan.J. S, B Manoj. · 2022
During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and Computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time.