Volume 39 Issue 1
Jan.  2021
Turn off MathJax
Article Contents
XU Ying-chun. Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34
Citation: XU Ying-chun. Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34

Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm

doi: 10.19695/j.cnki.cn12-1369.2021.01.34
  • Received Date: 2020-11-02
  • Rev Recd Date: 2021-01-17
  • Available Online: 2021-09-23
  • Publish Date: 2021-01-25
  • Target detection is a hot research direction in the field of computer vision, and traffic sign recognition has an important application in automatic driving. However, the detection accuracy of small targets in complex scenes is not high. To solve this problem, an improved algorithm based on YOLOv3 is proposed. By modifying the appropriate anchor size, Image Mixup is used to enlarge the image and increase the number of positive samples in the prediction box; Resnet50-d is used to enhance the ability of feature extraction; three scale feature maps are output from the network structure, and the feature maps are fused across scales by upsampling. The improved algorithm is compared with the source algorithm on the dataset. The results show that the improved algorithm can effectively improve the accuracy of target detection.

     

  • loading
  • [1]
    Fukushima K,Hayashi I,L é veill é J.Neocognitron trained by winner-kill-loser with triple threshold[J].Neurocomputing,2014(129):78-84.
    [2]
    LeCun Y,Bengio Y,Hinton G.Deep learning[J].nature,2015,521(7553):436-444.
    [3]
    胡伏原,李林燕,尚欣茹,等.基于卷积神经网络的目标检测算法综述[J].苏州科技大学学报(自然科学版),2020,37(2):1-10+25.
    [4]
    Lin M,Chen Q,Yan S.Network in network[J].arXiv preprint arXiv:1312.4400,2013.
    [5]
    陈超,齐峰.卷积神经网络的发展及其在计算机视觉领域中的应用综述[J].计算机科学,2019,46(3):63-73.
    [6]
    Zeiler M D,Fergus R.Visualizing and understanding convolutional networks[C]//European conference on computer vision. Springer,Cham,2014:818-833.
    [7]
    Jaderberg M,Simonyan K,Zisserman A.Spatial transformer networks[C]//Advances in neural information processing systems. 2015:2017-2025.
    [8]
    Krizhevsky A,Sutskever I,Hinton G E.Imagenet classification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84-90.
    [9]
    Hinton G E,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].arXiv preprint arXiv:1207.0580,2012.
    [10]
    Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[J].arXiv preprint arXiv:1409.1556,2014.
    [11]
    Szegedy C,Vanhoucke V,Ioffe S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:2818-2826.
    [12]
    Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions [C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2015:1-9.
    [13]
    Ioffe S,Szegedy C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv preprint arXiv:1502.03167,2015.
    [14]
    He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2016:770-778.
    [15]
    Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2014:580-587.
    [16]
    Uijlings J R R,Van De Sande K E A,Gevers T,et al.Selective search for object recognition[J].International journal of computer vision,2013,104(2):154-171.
    [17]
    Bodla N,Singh B,Chellappa R,et al.Soft-NMS--improving object detection with one line of code[C]//Proceedings of the IEEE international conference on computer vision. 2017:5561-5569.
    [18]
    GIRSHICK R.Fast R-CNN[C].IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:1440-1448.
    [19]
    Ren S,He K,Girshick R,et al.Faster r-cnn:Towards realtime object detection with region proposal networks[C]//Advances in neural information processing systems.2015:91-99.
    [20]
    Lin T Y,Doll á r P,Girshick R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2017:2117-2125.
    [21]
    Jiang B,Luo R,Mao J,et al.Acquisition of localization confidence for accurate object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:784-799.
    [22]
    Cai Z,Vasconcelos N.Cascade r-cnn:Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2018:6154-6162.
    [23]
    Li Y,Chen Y,Wang N,et al.Scale-aware trident networks for object detection[C]//Proceedings of the IEEE international conference on computer vision.2019:6054-6063.
    [24]
    Redmon J,Divvala S,Girshick R,et al.You only look once: Unified,real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2016:779-788.
    [25]
    Redmon J,Farhadi A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2017:7263-7271.
    [26]
    Redmon J,Farhadi A.YOLOv3:an incremental improvement [J].arXiv:1804.02767,2018.
    [27]
    Bochkovskiy A,Wang C Y,Liao H Y M.YOLOv4:Optimal Speed and Accuracy of Object Detection[J].arXiv preprint arXiv:2004. 10934,2020.
    [28]
    Zhou X,Wang D,Kr henb ü hl P.Objects as points[J].arXiv preprint arXiv:1904.07850,2019.
    [29]
    Tian Z,Shen C,Chen H,et al.Fcos:Fully convolutional onestage object detection[C]//Proceedings of the IEEE international conference on computer vision.2019:9627-9636.
    [30]
    Liu Y,Wang Y,Wang S,et al.CBNet:A Novel Composite Backbone Network Architecture for Object Detection[C]//AAAI.2020: 11653-11660.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (299) PDF downloads(37) Cited by()
    Proportional views
    Related
    Copyright © Editorial Department of Digital Technology and Application Supported by: Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return