MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||4|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
The implementation of an object detection algorithm on the FT processor
School of Computer, National University of Defense Technology, Changsha, China
* Corresponding author: email@example.com
With the continuous development of automatic drive and neural networks, it is possible to use neural network algorithm to carry out object detection in unmanned driving. Usually, the computation of neural network algorithm is huge. How to efficiently compute the algorithm and meet the real-time requirement is a challenge. In this paper, a sparse neural network algorithm is proposed, which can improve the utilization rate of processors. The object detection algorithm YOLO is implemented on the processor. Its performance is equivalent to the current best processor performance.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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