Road traffic sign recognition algorithm based on improved YOLOv4

LI Daxiang; SU Zhongheng; LIU Ying

doi:10.37188/OPE.20233109.1366

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Road traffic sign recognition algorithm based on improved YOLOv4

Information Sciences | 更新时间：2023-05-15

- Road traffic sign recognition algorithm based on improved YOLOv4
- Optics and Precision Engineering Vol. 31, Issue 9, Pages: 1366-1378(2023)
- 作者机构：
  
  西安邮电大学通信与信息工程学院，陕西西安 710121
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233109.1366
  CLC： TP394.1
- Received：13 July 2022，
  
  Revised：08 August 2022，
  
  Published：10 May 2023
- 稿件说明：
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李大湘,苏仲恒,刘颖.基于改进YOLOv4的道路交通标志识别[J].光学精密工程,2023,31(09):1366-1378.

LI Daxiang,SU Zhongheng,LIU Ying.Road traffic sign recognition algorithm based on improved YOLOv4[J].Optics and Precision Engineering,2023,31(09):1366-1378.
李大湘,苏仲恒,刘颖.基于改进YOLOv4的道路交通标志识别[J].光学精密工程,2023,31(09):1366-1378. DOI： 10.37188/OPE.20233109.1366.

LI Daxiang,SU Zhongheng,LIU Ying.Road traffic sign recognition algorithm based on improved YOLOv4[J].Optics and Precision Engineering,2023,31(09):1366-1378. DOI： 10.37188/OPE.20233109.1366.

摘要

针对复杂场景中交通标志尺度变化大导致识别精度低的问题，提出了一种改进的YOLOv4算法。首先，设计了一个注意力驱动的尺度感知特征提取模块，通过构建类似残差结构的分层连接方式，增加每层的感受野范围，以获得更具细粒度的多尺度特征，并在注意力驱动下生成一对具有方向感知与位置敏感的注意力图，使网络能聚焦于更具鉴别力的关键区域；然后，构建一个特征对齐的金字塔卷积特征融合模块，即通过卷积计算相邻尺度特征图间的特征偏移量进行特征对齐；最后，通过金字塔卷积的方式使网络自适应学习最优的特征融合模式，并构建特征金字塔用于识别不同尺度的交通标志。实验结果表明，在TT100K数据集上改进算法比原YOLOv4算法的识别精度提高了5.4%，且优于其他对比识别算法，FPS达到33.17，可满足道路交通标志识别的精确性、实时性等要求。

Abstract

To address the low recognition accuracy resulting from multiple scale changes in the traffic signs of complex scenes， an improved YOLOv4 algorithm is proposed. First， an attention-driven scale-aware feature extraction module is designed， and the range of receptive fields in each layer is widened to obtain more fine-grained multi-scale features by constructing a hierarchical connection mode similar to the residual structure； this is followed by the generation of a pair of attention maps with directional-aware and position-sensitive characteristics under the attention drive so that the network can focus on key areas with more discrimination. Following this， a feature-aligned pyramid convolution feature fusion module is constructed， and the feature offset between adjacent scale feature maps is obtained via convolution for feature alignment. Finally， the network adaptively learns the optimal feature fusion mode through pyramid convolution and constructs a feature pyramid to identify traffic signs with different scales. Experimental results indicate that the recognition accuracy for the TT100K dataset is improved by 5.4% compared with that of the original YOLOv4 algorithm， which is superior to other recognition algorithms， and the FPS reaches 33.17. Thus， the proposed algorithm satisfies the requirements of accuracy and real-time performance for road traffic sign recognition.

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references

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