Lane Detection Using Adaptive Thresholding and Segmentation
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Solution Overview
Problem
Conventional lane detection systems face challenges in accurately detecting lane markings at longer distances and handling varying road conditions, including curvy and dashed markings, under medium to low illumination and noisy scenarios, which affects their accuracy and reliability for driver assistance systems.
Innovation Solution
A computer-implemented method and system for lane detection that generates edge images from road images, extracts lane masks using adaptive thresholding, selects valid lane masks based on structure and intensity information, and determines edge segments with curvature information to identify lane markings, enhancing detection accuracy and distance coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection and global segmentation techniques are used, then lane detection works well in near view and straight lanes, but detection accuracy deteriorates at longer distances and for curvy/dashed markings
Solution Approach 1:
The patent divides the road image into multiple horizontal strips and processes each strip independently to extract lane masks. This segmentation approach allows the system to handle different road conditions in different regions, improving overall detection accuracy for curvy and dashed markings while maintaining performance at various distances.
Solution Approach 2:
The patent employs adaptive thresholding that dynamically adjusts threshold values based on local image characteristics in each horizontal strip. This dynamic adaptation enables the system to maintain high detection accuracy across varying illumination conditions, road surfaces, and lane marking types (solid, dashed, curvy) without requiring manual recalibration.
2Reliability
If adaptive thresholding is applied to extract lane masks, then detection accuracy improves in noisy conditions, but computational complexity increases
Solution Approach 1:
By dividing the image into horizontal strips and processing them independently, the computational complexity of adaptive thresholding is reduced. Each strip requires less processing power than the entire image, enabling real-time operation on embedded automotive platforms while maintaining reliability in noisy conditions through localized adaptive thresholding.
Solution Approach 2:
The patent applies adaptive thresholding selectively to extract only the necessary lane mask information from each horizontal strip, rather than processing the entire image with full complexity. This partial action approach maintains detection reliability while reducing overall computational burden for embedded systems.
3Measurement precision
If Hough line fitting is used for lane detection, then straight lane detection is accurate, but performance deteriorates for curvy and dashed lane markings
Solution Approach 1:
The patent segments the road image into horizontal strips and processes each strip independently to detect lane markings. This segmentation enables the system to handle curvy and dashed markings effectively by analyzing local patterns in each strip, rather than attempting to fit global Hough lines across the entire image, thereby improving adaptability while maintaining accuracy.
Solution Approach 2:
The patent uses adaptive thresholding and local pattern recognition that dynamically adjusts to detect various lane marking types (straight, curvy, dashed, solid) in each horizontal strip. This dynamic approach replaces the static Hough line fitting method, enabling versatile detection across different lane configurations while maintaining precision.
Data Source
AI summary
This disclosure relates generally to lane detection, and more particularly to method and systems for lane detection. In one embodiment, a method for lane detection is includes generating an edge image including inbound edges of lane-markings corresponding to a road image. Based on an adaptive thresholding of intensity of lane-markings from image strips selected from the image, a plurality of lane masks are extracted. A plurality of valid lane masks are selected from the plurality of lane masks based on a structure and intensity information of the plurality of valid lane masks in the image strips. An overlap between the inbound edges and the valid lane masks is determined to identify a plurality of edge segments of the pair of inbound edges belonging to the lane-markings. A set of valid edge segments of the pair of inbound edges are obtained based on the curvature information of the edge segments.