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Human Recognition in a Collaborative Robot-Manipulator Working Area Based on MobileNetV2 Deep Neural Network in Real Time

Maksymova,
Svitlana,
Gurin,
Dmytro,
Yevsieiev,
Vladyslav,
Alkhalaileh,
Ahmad

Annotatsiya

The article deals with the development of a human recognition system in a collaborative robot-manipulator working area based on MobileNetV2 deep neural network. The purpose of the research is to implement an accurate and fast real-time recognition algorithm to improve security and work efficiency. Using the MobileNetV2 model allows you to achieve high accuracy with minimal resource consumption. The results of the experiments demonstrate the high reliability of the system in conditions of changing lighting and moving obstacles, which opens up new opportunities for the integration of recognition in industrial collaborative robot.


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