Coronavirus: Baidu Develops Open-Source AI To Identify People Not Wearing Masks

Harin


Amid the Coronavirus outbreak, Baidu has decided to develop an open-source AI to detect people who don’t wear face masks.

Amid the Coronavirus outbreak, Chinese companies are putting their efforts into preventing the virus from spreading. One of those companies is Baidu, one of China’s top search tools. The company has decided to develop an open-source AI to detect people who don’t wear face masks.

Because Coronavirus transmits through close contact between humans and humans. The virus spreads through droplets when one coughs or sneezes. That’s why China has required its citizens to put on face masks when going out on the streets, to public places like restaurants, malls, or using public transportation.

China has required its citizens to put on face masks when going out on the streets, to public places like restaurants, malls, or using public transportation.

However, to detect people who don’t wear masks among the crowds is a challenging task. And this is where Baidu’s tool steps in. According to the company, it has used 100,000 images to train its AI to reach an accuracy rate of 96.5%.

Given that many businesses and factories in the countries have gone back to work after the Chinese New Year holiday, it is important for companies to identify people without masks to limit the spread of the virus.

Although Baidu claims that ít new AI model is one of a kind, in fact, the company has already worked on different AI models capable of detecting faces.

According to WHO, in China, there are now over 51,000 confirmed cases with more than 1,600 deaths.

Last week, the government released an app to help people know if they were in close contact with anyone infected with the virus.

Companies in China are looking to provide tools as well as creating research funds for the battle against the deadly virus. Earlier this month, Baidu and e-commerce giant Alibaba developed open-source AI models to assist universities and health centers, improving the prediction times for the virus’ RNA structure.

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