A Novel Lightweight Traffic Sign Recognition Model Based on YOLOv5-Reference-Cited by-同舟云学术

A Novel Lightweight Traffic Sign Recognition Model Based on YOLOv5

Published:2023-05 Issue:5 Volume:149 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Li Wenju¹,Zhang Gan²,Cui Liu³

Affiliation:

1. Professor, School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China (corresponding author).

2. Master’s Candidate, School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.

3. Lecturer, School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-7461

Reference60 articles.

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2. An introduction to support vector machines and other kernel-based learning methods;Andrew A. M.;Kybernetes,2001

3. Research on traffic sign detection algorithms based on HSV color model and shape characteristics;Bai J.;J. Henan Sci. Technol.,2019

4. Batool, A., M. W. Nisar, J. H. Shah, M. A. Khan, and A. A. El-Latif. 2022. iELMNet: Integrating novel improved extreme learning machine and convolutional neural network model for traffic sign detection. New Rochelle, NY: Mary Ann Liebert Inc.

5. Bochkovskiy, A., C.-Y. Wang, and H.-Y. M. Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. New York: IEEE.

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