• DocumentCode
    1986571
  • Title

    Obstacle Detection with Deep Convolutional Neural Network

  • Author

    Hong Yu ; Ruxia Hong ; Xiaolei Huang ; Zhengyou Wang

  • Author_Institution
    Dept. of Inf. Sci., Nanchang Teachers Coll., Nanchang, China
  • Volume
    1
  • fYear
    2013
  • fDate
    28-29 Oct. 2013
  • Firstpage
    265
  • Lastpage
    268
  • Abstract
    The difficulty of obstacle detection is how to locate and separate the obstacle from the complex background. Traditional computer vision algorithms can not handle this problem very well due to the handcrafted designed features are vulnerable in complex background. In this article, we use deep convolutional neural network (CNN) to detect obstacle in complex scene. The deep architecture of the CNN guarantees the features learned by the network are rich and effective for detecting the obstacle. The results show that the model achieved a good performance.
  • Keywords
    collision avoidance; computer vision; feature extraction; neural nets; object detection; CNN; complex background; computer vision algorithms; deep convolutional neural network; handcrafted designed features; obstacle detection; obstacle separation; Accuracy; Biological neural networks; Feature extraction; Image color analysis; Image edge detection; Laser radar; convolutional neural network; deep architecture; obstacle detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
  • Conference_Location
    Hangzhou
  • Type

    conf

  • DOI
    10.1109/ISCID.2013.73
  • Filename
    6804986