Published August 3, 2024
| Version v1
Journal article
Open
MobileNetv2 Neural Network Model for Human Recognition and Identification in the Working Area of a Collaborative Robot
Creators
- 1. Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
- 2. Senior Developer Electronic Health Solution, Amman, Jordan
Description
The article considers the software implementation of the MobileNetV2 neural network model for human recognition and identification in the working area of a collaborative robot. A mathematical description of the MobileNetV2 operation is presented, in particular its architecture and principles of operation, which allow to achieve high accuracy with reduced computing costs. The process of implementing the model in Python using the PyCharm environment is described, and a number of tests were conducted to evaluate its effectiveness in real-time conditions. The test results demonstrate the high accuracy and speed of the model, which confirms its suitability for use in collaborative robot systems that interact with people.
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Additional details
References
- 1. Samoilenko, H., & et al. (2024). Review for Collective Problem-Solving by a Group of Robots. Journal of Universal Science Research, 2(6), 7-16.
- 2. Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.
- 3. Matarneh, R., Maksymova, S., Deineko, Z., & Lyashenko, V. (2017). Building robot voice control training methodology using artificial neural net. International Journal of Civil Engineering and Technology, 8(10), 523-532.
- 4. Lyashenko, V., Abu-Jassar, A. T., Yevsieiev, V., & Maksymova, S. (2023). Automated Monitoring and Visualization System in Production. International Research Journal of Multidisciplinary Technovation, 5(6), 9-18.
- 5. Abu-Jassar, A. T., Attar, H., Lyashenko, V., Amer, A., Sotnik, S., & Solyman, A. (2023). Access control to robotic systems based on biometric: the generalized model and its practical implementation. International Journal of Intelligent Engineering and Systems, 16(5), 313-328.
- 6. Al-Sharo Y., & et al. (2023). A Robo-hand prototype design gripping device within the framework of sustainable development. Indian Journal of Engineering, 20, e37ije1673.
- 7. Yevsieiev, V., & et al. (2024). The Sobel algorithm implementation for detection an object contour in the mobile robot's workspace in real time. Technical Science Research in Uzbekistan, 2(3), 23-33.
- 8. Nikitin, V., & et al. (2023). Traffic Signs Recognition System Development. Multidisciplinary Journal of Science and Technology, 3(3), 235-242.
- 9. Abu-Jassar, A., & et al. (2024). The Optical Flow Method and Graham's Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot's Workspace. Journal of Universal Science Research, 2(3), 64-75.
- 10. Nevliudov, I., & et al. (2023). Mobile Robot Navigation System Based on Ultrasonic Sensors. In 2023 IEEE XXVIII International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), IEEE, 1, 247-251.