Detection of highway warning signs in natural video images using color image processing and neural networks

This study reports on the development of a system that incorporates color image processing and neural networks to detect and locate highway warning signs in natural roadway images. Such a system could reduce the need for redundant or oversized signs by assisting drivers in acquiring roadway information. Transportation agencies could use such a system as the first step in an automated highway sign inventory system. Currently, a human operator must watch hours of highway videos to complete this inventory. While only warning signs were considered in this study, the procedure was designed to be easily adapted to all highway signs. The basic approach is to digitize a roadway image and segment this image, using a back-propagation neural network, into eight colors that are important to highway sign detection. Next, the system scans the image for color regions that may possibly represent highway warning signs. Upon finding possible warning sign regions, these regions are further analyzed by a second back-propagation neural network to determine if their shape corresponds to that of a highway warning sign