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117 / 2021-04-06 19:28:36
Traffic Sign Recognition Based on ZYNQ
CNN, Traffic sign recognition, ZYNQ
IOT and AI Techniques > Deep Learning
终稿
Juan Liu / ChangSha university of science and technology
Lijun Tang / ChangSha university of science and technology
Zhigang Zhang / ChangSha university of science and technology
Longhao Zheng / ChangSha university of science and technology
Feng Bin / ChangSha university of science and technology
Yongjun Wen / ChangSha university of science and technology
In the traffic field, the traffic sign detection and recognition system is an important part of ADAS (Advanced Driver Assistance System). In this paper, a research on traffic sign recognition is carried out, and a traffic recognition sign recognition network is established with the GTSRB data set as the research object. A network computing accelerator is designed with "ARM+FPGA" framework; the functional modules of each part of the network are reasonably divided; and high-level synthesis tools are used to optimize the design of loop unwinding and loop flow to improve throughput and improve recognition speed. The experimental results show that the network has a high accuracy rate in each classification. After optimization, the processing speed of the system under the 100MHz clock is increased by 5.59 times. This method provides an effective reference for the identification of other specific signs.

 
重要日期

  • 会议日期

    07月10日

    2021

    至

    07月12日

    2021

  • 05月10日 2021

    初稿截稿日期

  • 07月06日 2021

    注册截止日期

主办单位
长沙理工大学
协办单位
IEEE Electron Devices Society
IEEE
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