Laser & Optoelectronics Progress, Volume. 61, Issue 24, 2412004(2024)

Lightweight Traffic Sign Recognition and Detection Algorithm Based on Improved YOLOv5s

Fei Liu1, Yanfen Zhong1,2,3、*, and Jiawei Qiu1
Author Affiliations
  • 1School of Civil Engineering and Transportation, Nanchang Hangkong University, Nanchang 330063, Jiangxi , China
  • 2Jiangxi Intelligent Building Engineering Research Centre, Nanchang 330063, Jiangxi , China
  • 3Nanchang Hangkong University Intelligent Construction Research Centre, Nanchang 330063, Jiangxi , China
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    References(36)

    [2] Han Z B. Research on traffic sign recognition based on improved YOLOv3[D](2022).

    [12] Chen M T, Yu S. Research on traffic sign recognition based on improved YOLOV4 model[J]. Microelectronics & Computer, 39, 17-25(2022).

    [23] Huang J F. Cross-project software defect prediction based on machine learning[D](2021).

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    Fei Liu, Yanfen Zhong, Jiawei Qiu. Lightweight Traffic Sign Recognition and Detection Algorithm Based on Improved YOLOv5s[J]. Laser & Optoelectronics Progress, 2024, 61(24): 2412004

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Feb. 5, 2024

    Accepted: Apr. 30, 2024

    Published Online: Dec. 13, 2024

    The Author Email: Yanfen Zhong (70016@nchu.edu.cn)

    DOI:10.3788/LOP240672

    CSTR:32186.14.LOP240672

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