Curve contour detection based on Helmholtz principle and application to road white line detection

Abstract

We propose curve contour detection algorithm for road white line detection based on Helmholtz principle. White line detection is widely used in driver support systems used mainly in highway or major arterial road. As the common road will be the target of operational area for autonomous vehicle, it is thought to be necessary to develop a new detection algorithm that can deal with various types of road. This paper proposes model-less algorithm that is constructed on a new edge feature inspired by Helmholtz principle through the analysis of the limit of Hough transform. This feature is basically same as Hough defined feature of edge count on the line except two remarkable points. The one is the restriction of count area and the other is the way of count which affords to detect curve line as well as straight line. Implementation by convolutional neural network is explained and the relation between tunable parameters and the detection performance as well as the processing time are discussed. Comparison between conventional methods such as Hough transform or machine learned contour detection algorithm BEL is explained for test image and images taken by on-board camera to show the superiority of proposed algorithm. We demonstrate that proposed algorithm that can apply to diverse road environments but is hardly affected by noise can be realized.

Keywords

• contour
• edge
• white line
• detection
• model-less
• common road
• helmholtz principle