Project report on “workplace safety using AI & ML

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dc.contributor.author Shah, Ubaid
dc.contributor.author Shekasan, Owais A. (19DME20)
dc.contributor.author Attar, Amaan D. (19DME02)
dc.contributor.author Gharade, A. Rahim A. R. (19DME06)
dc.contributor.author Khan, Israr A. (19DME12)
dc.date.accessioned 2022-08-03T11:02:31Z
dc.date.available 2022-08-03T11:02:31Z
dc.date.issued 2022-05
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/3929
dc.description.abstract Artificial intelligence and machine learning models are mathematical algorithms trained using specific data and human expert input to replicate a decision, which would be made by a human when provided with that same information. Furthermore, computer vision will be the predominant part of our project. Computer vision is a field of artificial intelligence (A.I.) that allows computers and systems to obtain meaningful information from images, videos, and other visual inputs and make changes or provide suggestions based on that information. If artificial intelligence allows computers to think, then computer vision allows them to see, observe and comprehend. It works similarly to human vision. The system is designed and developed to reduce accident rates in the workplace. This technique will increase system efficiency in reducing accident rates, increasing the safety of operators and machines. This proposed system uses OpenCV libraries, deep-learning algorithms, and python programming language. It has various detection modes like apron detection, which ensures operators dressed code, machine components detection that ensures operation safety, and operator safety. Whenever an unsafe action occurs, or the operator does not follow safety rules, it is detected by the algorithm, sensed by the camera, which will trigger an alarm, ultimately lowering the risk of accidents. en_US
dc.language.iso en en_US
dc.publisher AIKTC en_US
dc.subject Project Report - ME en_US
dc.title Project report on “workplace safety using AI & ML en_US
dc.type Project Report en_US


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