YOLOv3-A：基于注意力机制的交通标志检测网络

郭璠; 张泳祥; 唐琎; 李伟清

doi:10.11959/j.issn.1000-436x.2021031

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YOLOv3-A：基于注意力机制的交通标志检测网络

学术论文 | 更新时间：2024-06-05

- YOLOv3-A：基于注意力机制的交通标志检测网络
- YOLOv3-A: a traffic sign detection network based on attention mechanism
- 通信学报 2021年42卷第1期页码：87-99
- 作者机构：
  
  中南大学自动化学院，湖南长沙 410083
- 作者简介：
  
  [ "郭璠（1982- ），女，湖南临澧人，博士，中南大学副教授、硕士生导师，主要研究方向为图像处理、计算机视觉、人工智能等。" ]
  [ "张泳祥（1994- ），男，河南安阳人，中南大学硕士生，主要研究方向为模式识别、图像处理等。" ]
  [ "唐琎（1966- ），男，湖南武冈人，博士，中南大学教授、博士生导师，主要研究方向为计算机视觉、机器人、嵌入式系统、智能信息处理等。" ]
  [ "李伟清（1997- ），男，河南信阳人，中南大学硕士生，主要研究方向为医学图像处理、机器学习等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61502537);湖南省自然科学基金资助项目(2018JJ3681);中南大学中央高校基本科研业务费专项基金资助项目(2020zzts567)
- DOI：10.11959/j.issn.1000-436x.2021031
  中图分类号： TP391.41
- 网络首发：2021-01，
  
  纸质出版：2021-01-25
- 稿件说明：
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郭璠, 张泳祥, 唐琎, 等. YOLOv3-A：基于注意力机制的交通标志检测网络[J]. 通信学报, 2021,42(1):87-99.

Fan GUO, Yongxiang ZHANG, Jin TANG, et al. YOLOv3-A: a traffic sign detection network based on attention mechanism[J]. Journal on Communications, 2021, 42(1): 87-99.
郭璠, 张泳祥, 唐琎, 等. YOLOv3-A：基于注意力机制的交通标志检测网络[J]. 通信学报, 2021,42(1):87-99. DOI： 10.11959/j.issn.1000-436x.2021031.

Fan GUO, Yongxiang ZHANG, Jin TANG, et al. YOLOv3-A: a traffic sign detection network based on attention mechanism[J]. Journal on Communications, 2021, 42(1): 87-99. DOI： 10.11959/j.issn.1000-436x.2021031.

摘要

为了解决已有YOLOv3算法对于存在小目标问题和背景复杂问题的交通标志检测任务会有较多的误检和漏检的问题，在YOLOv3算法的基础上，提出了目标检测的通道注意力方法和基于语义分割引导的空间注意力方法，形成 YOLOv3-A 算法。YOLOv3-A 算法通过对检测分支特征在通道和空间 2 个维度进行重新标定，使网络聚焦和增强有效特征，并抑制干扰特征，提高了算法的检测能力。在TT100K交通标志数据集上的实验表明，所提算法对小目标检测性能的改善尤为明显，相比于YOLOv3算法，所提算法的精度和召回率分别提升了1.9%和2.8%。

Abstract

To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background

based on the YOLOv3

a channel attention method for target detection and a spatial attention method based on semantic segmentation guidance were proposed to form the YOLOv3-A (attention) algorithm.The detection features in the channel and spatial dimensions were recalibrated

allowing the network to focus and enhance the effective features

and suppress interference features

which greatly improved the detection performance.Experiments on the TT100K traffic sign data set show that the algorithm improves the detection performance of small targets

and the accuracy and recall rate of the YOLOv3 are improved by 1.9% and 2.8% respectively.

关键词

Keywords

references

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