Covid19 is considered to be the most critical period for all countries. The novel corona virus is spreading in a faster manner. The government has taken various measures in order to avoid the spreading of virus by implementing lockdown across the whole country and they spray insecticides in various areas and keep the area clean. Though the government is taking several measures, some people are not cooperating with the rules formulated by the government and they violate the rules and increase the chances for spreading the virus. Many people do not follow the rules such as social distancing from each other, not wearing masks and does not follow lock down rules. This leads to worsen the health condition of the society members. In order to make people to follow the above constraints framed by the government, the proposed system is designed in such a way that the above rules are being followed by people.

What it does

The proposed system uses an automated drone which is used to perform the inspection process. First, the drone is being constructed by considering the parameters such as components selection, payload calculation and then assembling the drone components and connecting the drone with the mission planner software for calibrating the drone for its stability. The trained yolov3 algorithm with the custom dataset is being embedded in the drone's camera. The drone camera runs the yolov3 algorithm and detects the social distance is maintained or not and whether the people in public is wearing masks or not. This process is carried out by the drone automatically. The proposed system delivers masks to people who are not wearing masks and tells importance of masks and social distancing. Thus, this proposed system would work in an efficient manner after the lockdown period ends and helps in easy social distance inspection in an automatic manner. The algorithm can be embedded in public cameras and then details can be fetched to the camera unit same as the drone unit which receives details from the drone location details and store it in database. Thus, the proposed system favours the society by saving time and helps in lowering the spread of corona virus.

How we built it

We have first used yolov3 algorithm for mask and social distance. After that we used IBM cloud services such as node-red, visual recognition, tello drone , text to speech services. Then we have used pega platform to build an application for scheduling the inspection process

Challenges we ran into

Due to covid19 pandemic situation, we do not have a drone.

Accomplishments that we're proud of

1) Successful detection of social distance, mask non mask and no social distance using ibm cloud service and yolov3 2) Deployed Drone ui in node red 3) built application using pega and created survey reports and storage of data in pega database

What we learned

we learnt about yolov3 deep learning algorithm ,ibm cloud services such as node-red, visual recognition, text to speech , ibm tello drone services and pega platform

What's next for Social Distance Inspection through Drone -IBM Services & PEGA

To implement this project in a drone for successful prediction of social distance and enhance the application which is used for the inspection process

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