Lane Line Detection Using Candidate Pixel Extension
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing lane line detection technologies require significant computational resources and often fail to accurately detect lane lines far from the vehicle, posing a challenge for quick and accurate decision-making in driving scenarios.
Innovation Solution
A method involving the use of a confidence map to identify candidate pixels, selecting target pixels, and extending lane lines based on associated location points to quickly and accurately detect lane lines, including those far from the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge detection and edge point extraction methods are used to detect lane lines, then lane line detection can be performed, but a large amount of computation is required which consumes computation resources and may cause inaccuracy
Solution Approach 1:
The patent extracts only the necessary information (lane line region and surrounding region) from the original image through candidate pixel selection, rather than processing the entire image with edge detection algorithms. This extraction approach reduces computation burden while maintaining detection accuracy.
Solution Approach 2:
The patent segments the lane line detection task into multiple stages: obtaining candidate pixels in the lane line region, selecting target pixels, and extending to obtain lane line point sets. This segmentation allows efficient processing by focusing computation only on relevant areas.
2Measurement precision
If traditional edge detection methods are used, then lane line detection can be achieved, but lane lines far away from the vehicle cannot be accurately detected
Solution Approach 1:
The patent performs preliminary action by pre-obtaining candidate pixels in the lane line region and pre-selecting target pixels before extension. This preliminary processing ensures that both near and far lane lines are accurately detected by establishing a foundation of pre-processed candidate data.
Solution Approach 2:
The patent transitions from 2D edge detection to a multi-dimensional approach by obtaining at least three location points in the neighborhood of the target pixel and performing extension in multiple directions. This dimensional expansion enables accurate detection of lane lines at various distances.
3Reliability
If comprehensive edge extraction is performed to improve detection accuracy, then more lane line information can be obtained, but computation time increases
Solution Approach 1:
The patent applies local quality by focusing computation on the lane line region and surrounding region where candidate pixels are obtained, rather than processing the entire image. This localized approach maintains detection reliability while significantly reducing processing time.
Solution Approach 2:
The patent uses partial action by obtaining only the necessary candidate pixels and location points required for lane line detection, rather than performing comprehensive edge extraction on the entire image. This partial approach achieves sufficient accuracy without excessive computation time.
Data Source
AI summary
A method comprises: first, obtaining a to-be-recognized lane line image; then determining, based on the lane line image, a candidate pixel used to recognize a lane line region, to obtain a candidate point set, where the lane line region is a region of a location of a lane line in the lane line image and a surrounding region of the location of the lane line; then selecting a target pixel from the candidate point set, and obtaining at least three location points associated with the target pixel in a neighborhood, where the at least three location points are on one lane line; and finally, performing extension by using the target pixel as a start point and based on the at least three location points associated with the target pixel, to obtain a lane line point set corresponding to the target pixel.