In this paper, a new effective method for automatic detection of circular traffic signs is introduced. Automatic traffic sign recognition is a crucial application of Driver Assistance Systems for safe and comfortable driving conditions. The major step of traffic sign recognition is to detect and localize the traffic signs if they exist. An important portion of traffic signs (i.e. regulatory traffic signs) has circular shape. They include vital information about the traffic rules and regulations (especially the speed limits). Therefore, this study introduces an advanced circular detection method to detect and localize the circular traffic signs. In the previous literature, it is known that a circle creates a distinctive sinusoidal structure in Straight Line Hough Transform (SLHT). This study exploits this notion in circle detection by trying to catch a part of the envelopes of this sinusoidal structure. First, post edge detection is applied to SLHT, and this image is called H-Edge image. Then, edge linking is performed on H-Edge image to obtain multiple candidate curves. By sinusoid curve fitting and sinusoidal normalization, the curves belonging to the sinusoidal structure are identified, so the circle in the original image is detected. Furthermore, a new effective iterative linear image segmentation method which is based on local minima in SLHT is proposed. Combining these two methods and color filtering, a new effective method for circular traffic sign detection is obtained. For certain sample images, the new method effectively detects and localizes the existing circular traffic signs.
Automatic Traffic Sign Detection, Advanced Driver Assistant Systems, Circle Detection, Image Segmentation, Straight Line Hough Transform