Adaptive road aerial view transformation method and road lane detection method

Abstract

The invention discloses an adaptive road aerial view transformation method and a road lane detection method. The transformation method comprises the steps: road boundary feature extraction, road boundary segmentation extraction, road boundary point extraction, road boundary modeling and matching, the obtaining of an aerial view transformation matrix through the transformation of adaptive aerial view transformation, and the mapping of a road image gray scale map to an aerial view map. The detection method comprises the steps: firstly carrying out the marking and filtering of lanes, and carrying out feature extraction and the estimation of the positions of the lane marks; and secondly extracting the positions of the lane marks in a road scene image through the positions of the lane marks The methods are better in robustness, and be used for processing a road surface comprising a large amount of shadow interference, solve a problem that there are fixed transformation parameters in a conventional method, can automatically adjust the parameters of a system, and extract the positions of lane mark lines more accurately.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to an adaptive road bird's-eye view transformation method and a method for detecting road lanes based on the method. Background technique [0002] Intelligent vehicle perception can save human life and reduce economic losses, among which road detection is an integral part of intelligent vehicle perception. Automobile active safety technology enables automobiles to proactively detect accidents, and solves the defect that traditional automobile safety can only passively mitigate accident injuries after an accident occurs. One of the key technologies of automotive active safety is environmental perception, which aims to obtain surrounding information by analyzing data collected by sensors. The most basic task in driving is to follow the road, so road detection plays an important role in vehicle perception and has great research significance for traffic safety issues. Most roads have whi...

AI Technical Summary

Problems solved by technology

Calculating the transformation matrix through calibration experiments before use not only makes the equipment installation cumbersome, but also has potential problems: on the one hand, if the camera shakes or the position moves, the original transformation matrix will not work, resulting in detection results Deviation or even error; on the other hand, even if the camera is fixed, the transformation will be inaccurate when the vehicle goes up and downhill and is not parallel to the road. , the road area is transformed into a parallelogram

Therefore, the problems of the existing model-based methods are mainly divided into two aspects, one is the need to calibrate in advance, and the other is potential problems: on the one hand, the camera needs to be re-calibrated after moving, and on the other hand, distortion will occur even if the camera is kept fixed

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