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Face Mask Detection Using Transfer Learning and TensorRT Optimization

Publication Type : Conference Paper

Publisher : SpringerLink

Source : International Conference on Innovative Computing and Communications

Url :

Campus : Coimbatore

School : School of Artificial Intelligence - Coimbatore

Year : 2023

Abstract : TensorRT is a high-performance deep learning inference optimizer and runtime that can be used to speed up the deployment of deep learning models. In this paper, the performance of different neural network architectures when using TensorRT is compared and showed that TensorRT can significantly reduce the inference time of deep learning models on embedded systems. The SARS-CoV-2 virus, which causes the infectious disease COVID-19, has had a significant impact on global health and the economy. Non-pharmaceutical interventions such as wearing face masks are an effective way to reduce the spread of the virus, and automatic detection systems based on CNN’s can help to detect mask-wearing without requiring human intervention which saves resources or manpower deployed. The results demonstrate that TensorRT is a valuable tool for deploying deep learning applications in resource-constrained environments and can help to improve the performance of a wide range of neural network architectures.

Cite this Research Publication : Chowdary, P.N. et al. (2023). Face Mask Detection Using Transfer Learning and TensorRT Optimization. In: Hassanien, A.E., Castillo, O., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. ICICC 2023. Lecture Notes in Networks and Systems, vol 703. Springer, Singapore.

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