SHS Web Conf.
Volume 77, 2020The 2nd ACM Chapter International Conference on Educational Technology, Language and Technical Communication (ETLTC2020)
|Number of page(s)
|08 May 2020
Design and Optimization of a Deep Neural Network Architecture for Traffic Light Detection
Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan
* Corresponding Author: Tomohide Fukuchi e-mail: firstname.lastname@example.org
Autonomous Driving has recently become a research trend and efficient autonomous driving system is difficult to achieve due to safety concerns, Applying traffic light recognition to autonomous driving system is one of the factors to prevent accidents that occur as a result of traffic light violation. To realize safe autonomous driving system, we propose in this work a design and optimization of a traffic light detection system based on deep neural network. We designed a lightweight convolution neural network with parameters less than 10000 and implemented in software. We achieved 98.3% inference accuracy with 2.5 fps response time. Also we optimized the input image pixel values with normalization and optimized convolution layer with pipeline on FPGA with 5% resource consumption.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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