Abstract
Today, the field of artificial intelligence (AI) is increasingly developing with the development of the 4.0 revolution. The applicability for the social life of AI and deep learning is indeed enormous. Convolutional neural network (CNN) models have many advantages for object detection and classification problems. In the paper, we present the results of real-time object recognition algorithms on Jetson Nano hardware. We perform the algorithm to recognize and deploy on GPU with largest optimal rate as 76.26%. The results show that Mobilenetv2 and YOLOv3 models are the most optimal for object recognition with the processing time of 50 and 51 milliseconds, respectively.
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Huu, P.N., Ngoc, T.P., Hai, T.L.T. (2022). Developing Real-time Recognition Algorithms on Jetson Nano Hardware. In: Anh, N.L., Koh, SJ., Nguyen, T.D.L., Lloret, J., Nguyen, T.T. (eds) Intelligent Systems and Networks. Lecture Notes in Networks and Systems, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-19-3394-3_6
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DOI: https://doi.org/10.1007/978-981-19-3394-3_6
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