Vehicle driving lane positioning method based on semantic segmentation

Abstract

The invention discloses a vehicle driving lane positioning method based on semantic segmentation. The method comprises the steps of firstly, constructing a double-lane semantic segmentation network onthe basis of a Segnet network after a road image is acquired; performing feature extraction on the road image; outputting a lane segmentation mask image of the road image; judging whether each pixelpoint on the road image belongs to a left lane, a right lane or a non-lane; carrying out target detection on a vehicle in the road image; acquiring the position of the vehicle on the road image; and finally, by fusing the lane segmentation mask image and a vehicle target detection result, judging the lane where the vehicle is located. According to the method, an encoder-decoder architecture network is adopted to realize end-to-end training of a double-lane semantic segmentation model; and the requirement of detection timeliness is met.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a vehicle lane positioning method based on semantic segmentation. Background technique [0002] Visual perception plays a key role in the development of assisted driving systems in vehicles. In the vehicle assisted driving system, image processing and computer vision technology are used to ensure a safe distance between vehicles and the correct lane, and to respond and deal with some abnormal conditions in a timely manner. Detect and segment lanes and vehicles, and locate the lane in which the vehicle is driving to assist vehicle driving. [0003] Most of the traditional vehicle lane location methods are realized by lane line detection. The lane line is detected, and the vehicle driving lane is located according to the position of the lane line. Some current technical solutions are: lane line detection based on Hough transform, using the characteristic that the road ed...

AI Technical Summary

Problems solved by technology

[0004] At present, the commonly used lane line detection based on Hough transform has poor anti-interference, and the complex environment may lead to excessive calculation; lane line detection based on threshold segmentation requires a high threshold for judgment; lane line detection based on template matching needs to find different templates

The traditional method uses lane line detection to achieve, the robustness is not good, and the effect is only good in a specific environment

In lane segmentation, existing methods do not segment specific lanes

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