• DocumentCode
    3280290
  • Title

    Hazardous material sign detection and recognition

  • Author

    Parra, A. ; Bin Zhao ; Haddad, Ali ; Boutin, Mireille ; Delp, Edward J.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
  • fYear
    2013
  • fDate
    15-18 Sept. 2013
  • Firstpage
    2640
  • Lastpage
    2644
  • Abstract
    In this paper we describe two methods for hazardous material (hazmat) sign recognition. The first method is based on segment detection and grouping using geometric constraints. The second method is based on the use of a saliency map and convex quadrilateral detection. Our experimental results show a detection accuracy of 57.7% on a set of hazmat signs taken in the field under various lightning conditions, distances, and perspectives.
  • Keywords
    computational geometry; hazardous materials; image segmentation; object detection; object recognition; convex quadrilateral detection; geometric constraints; hazardous material sign detection; hazardous material sign recognition; hazmat sign detection; hazmat sign recognition; saliency map; segment detection; segment grouping; Hough Transform; Sign detection; saliency map; shape detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2013 20th IEEE International Conference on
  • Conference_Location
    Melbourne, VIC
  • Type

    conf

  • DOI
    10.1109/ICIP.2013.6738544
  • Filename
    6738544
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3280290