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The Journal of Supercomputing

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06-08-2022

Traffic sign detection based on multi-scale feature extraction and cascade feature fusion

Authors: Yongliang Zhang, Yang Lu, Wuqiang Zhu, Xing Wei, Zhen Wei

Published in: The Journal of Supercomputing | Issue 2/2023

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Abstract

Existing algorithms have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic images of complex traffic scenes. In order to solve the above problems, a new road traffic sign dataset is created, and based on the YOLOv4 algorithm, for the complexity of realistic traffic scene images and the large variation in the size of traffic signs in the images, the multi-scale feature extraction module, cascade feature fusion module and attention mechanism module are designed to improve the algorithm’s ability to locate and classify traffic signs simultaneously. Experimental results on the newly created dataset show that the improved algorithm achieves a mean average precision of 84.44%, which is higher than several major CNN-based object detection algorithms for the same type of task.

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Title: Traffic sign detection based on multi-scale feature extraction and cascade feature fusion
Authors: Yongliang Zhang
Yang Lu
Wuqiang Zhu
Xing Wei
Zhen Wei
Publication date: 06-08-2022
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 2/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-04670-6

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