Real Time Face detection Method Using TensorRT and SSD


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 10, pp. 323-328, Oct. 2020
https://doi.org/10.3745/KTSDE.2020.9.10.323, Full Text:
Keywords: Tensorflow, TensorRT, Deep Learning, SSD, Object Detection
Abstract

Recently, new approaches that significantly improve performance in object detection and recognition using deep learning technology have been proposed quickly. Of the various techniques for object detection, especially facial object detection (Faster R-CNN, R-CNN, YOLO, SSD, etc), SSD is superior in accuracy and speed to other techniques. At the same time, multiple object detection networks are also readily available. In this paper, among object detection networks, Mobilenet v2 network is used, models combined with SSDs are trained, and methods for detecting objects at a rate of four times or more than conventional performance are proposed using TensorRT engine, and the performance is verified through experiments. Facial object detector was created as an application to verify the performance of the proposed method, and its behavior and performance were tested in various situations.


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Cite this article
[IEEE Style]
H. Yoo, M. Park and S. Kim, "Real Time Face detection Method Using TensorRT and SSD," KIPS Transactions on Software and Data Engineering, vol. 9, no. 10, pp. 323-328, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.10.323.

[ACM Style]
Hye-Bin Yoo, Myeong-Suk Park, and Sang-Hoon Kim. 2020. Real Time Face detection Method Using TensorRT and SSD. KIPS Transactions on Software and Data Engineering, 9, 10, (2020), 323-328. DOI: https://doi.org/10.3745/KTSDE.2020.9.10.323.