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
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;
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICIP.2013.6738544
