YOLOv3-A: a traffic sign detection network based on attention mechanism

Fan GUO; Yongxiang ZHANG; Jin TANG; Weiqing LI

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

您当前的位置：

首页 >

文章列表页 >

YOLOv3-A: a traffic sign detection network based on attention mechanism

Papers | 更新时间：2024-06-05

- YOLOv3-A: a traffic sign detection network based on attention mechanism
- Journal on Communications Vol. 42, Issue 1, Pages: 87-99(2021)
- 作者机构：
  
  中南大学自动化学院，湖南长沙 410083
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61502537);The Natural Science Foundation of Hunan Province(2018JJ3681);The Fundamental Research Funds for the Central Universities of Central South University(2020zzts567)
- DOI：10.11959/j.issn.1000-436x.2021031
  CLC： TP391.41
- Online First：2021-01，
  
  Published：25 January 2021
- 稿件说明：
移动端阅览
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：

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

DALAL N , TRIGGS B . Histograms of oriented gradients for human detection [C ] // 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2005 : 886 - 893 .

LEE T S . Image representation using 2D Gabor wavelets [J ] . IEEE Transactions on Pattern Analysis ＆ Machine Intelligence , 1996 , 18 ( 10 ): 959 - 971 .

VIOLA P A , JONES M J . Rapid object detection using a boosted cascade of simple features [C ] // IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2001 : 511 - 518 .

REN S , HE K , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once:Unified,real-time object detection [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 779 - 788 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD:single shot multibox detector [C ] // European Conference on Computer Vision . Berlin:Springer , 2016 : 21 - 37 .

RAJENDRAN S P , SHINE L , PRADEEP R , et al . Fast and accurate traffic sign recognition for self driving cars using RetinaNet based detector [C ] // 2019 International Conference on Communication and Electronics Systems . Piscataway:IEEE Press , 2019 : 784 - 790 .

LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [C ] // 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 2980 - 2988 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

HOUBEN S , STALLKAMP J , SALMEN J , et al . Detection of traffic signs in real-world images:the German traffic sign detection benchmark [C ] // The 2013 International Joint Conference on Neural Networks . Piscataway:IEEE Press , 2013 : 1 - 8 .

YANG Y , LIU S , MA W , et al . Efficient traffic-sign recognition with scale-aware CNN [C ] // British Machine Vision Conference . London:BMVA Press , 2017 : 1 - 13 .

LARSSON F , FELSBERG M . Using Fourier descriptors and spatial models for traffic sign recognition [C ] // 2011 Scandinavian Conference on Image Analysis(SCIA 2011) . Berlin:Springer , 2011 : 238 - 249 .

MENG Z , FAN X , CHEN X , et al . Detecting small signs from large images [C ] // 2017 IEEE International Conference on Information Reuse and Integration . Piscataway:IEEE Press , 2017 : 217 - 224 .

REDMON J , FARHADI A . YOLOv3:an incremental improvement [J ] . arXiv Preprint,arXiv:1804.02767 , 2018 .

LIN T Y , DOLLÁR P , GIRSHICK R , et al . Feature pyramid networks for object detection [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 2117 - 2125 .

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 42 ( 8 ): 2011 - 2023 .

WANG F , JIANG M , QIAN C , et al . Residual attention network for image classification [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 6450 - 6458 .

HOU Y , MA Z , LIU C , et al . Learning lightweight lane detection CNNs by self-attention distillation [C ] // 2019 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2019 : 1013 - 1021 .

ZHANG X , WANG T , QI J , et al . Progressive attention guided recurrent network for salient object detection [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 714 - 722 .

LONG J , SHELHAMER E , DARRELL T . Fully convolutional networks for semantic segmentation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 4 ): 640 - 651 .

HE K , GKIOXARI G , DOLLÁR P , et al . Mask R-CNN [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 42 ( 2 ): 386 - 397 .

SZEGEDY C , LIU W , JIA Y , et al . Going deeper with convolutions [C ] // 2015 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2015 : 1 - 9 .

CHEN L C , PAPANDREOU G , SCHROFF F , et al . Rethinking atrous convolution for semantic image segmentation [J ] . arXiv Preprint,arXiv:1706.05587 , 2017 .

UIJLINGS J R R , VAN D S K E A , GEVERS T , et al . Selective search for object recognition [J ] . International Journal of Computer Vision , 2013 , 104 ( 2 ): 154 - 171 .

ZHU Z , LIANG D , ZHANG S , et al . Traffic-sign detection and classification in the wild [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 2110 - 2118 .

LARSSON F , FELSBERG M , FORSSEN P E . Correlating Fourier descriptors of local patches for road sign recognition [J ] . IET Computer Vision , 2011 , 5 ( 4 ): 244 - 254 .

MOGELMOSE A , TRIVEDI M M , MOESLUND T B . Vision based traffic sign detection and analysis for intelligent driver assistance systems:perspectives and survey [J ] . IEEE Transactions on Intelligent Transportation Systems , 2012 , 13 ( 4 ): 1484 - 1497 .

SERMANET P , EIGEN D , ZHANG X , et al . Overfeat:integrated recognition,localization and detection using convolutional networks [J ] . arXiv Preprint,arXiv:1312.6229 , 2013 .

Views

3415

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution

Illicit data resale detection method via an enhanced relational graph convolutional network

Network traffic anomaly detection method based on multi-scale convolution and channel attention mechanism

Trajectory reconstruction attacks on differential privacy based on a CNN-BiLSTM-Attention hybrid model

THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network

Related Author

Gang XIE

Quanyi WANG

Xinlin XIE

Jian’an WANG

Wang Yuxiang

Zhang Lingcui

Hou Yuqiao

Yang Qian

Related Institution

School of Electronic and Information Engineering, Taiyuan University of Science and Technology

Shanxi Key Laboratory of Advanced Control and Equipment Intelligence

State Key Laboratory of Cyberspace Security Defense

School of Cyber Security, University of Chinese Academy of Sciences

Institute of Information Engineering, Chinese Academy of Sciences

AI问答

⁰