Multi-interference Lane Detection Based on IPM and Edge Image Filtering

Abstract

In visual based environment perception for autopilots, shadows, stains, water and reflected light could interfere with lane recognition and navigation. In this paper, an improved lane recognition algorithm based on inverse perspective mapping (IPM) and edge image filtering was proposed to solve this issue. An aerial image of the original road scene could be obtained though IPM, which could significantly enhance the visual characteristics of the lane and reduce interference. An iterative clustering segmentation method was proposed to analyze the grayscale values of IPM gray image, and the gray points closest to the color and morphological features of the lane were retained as the lane edge in IPM image. Subsequently, a method that could search and determine the statistic of continuous edge regions was developed to segment the edge image. Filtering the interference factors was achieved by analyzing the edge points and retaining the longest regions. In comparison with other commonly used lane recognition algorithms, the result indicates that our method can more effectively filter all kinds of interference factors on the road and enhance the ability to detect fuzzy, real, virtual, and curved lanes under an environment with interference. This significantly improves the ability to keep to a lane under an autopilot environment. Because of this, lane recognition speed is greatly improved, which can meet the requirement of real-time autopilot.

Tasks

Reproductions