Lane Marking Detection Using Active Contour Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lane marking recognition technologies struggle to accurately detect both linear and curved lane markings in real-time from vehicle images, often leading to errors due to adjacent vehicles, obstacles, and varying road conditions, which can result in lane departure accidents and poor augmented reality navigation experiences.
Innovation Solution
A vision-based method that includes acquiring vehicle images, detecting edges, generating edge images, and using active contour models to identify lane markings, transforming views to improve detection accuracy, and adapting search window sizes based on lane inclination for precise linear and curved lane marking recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If curved lane markings are detected using color and edge information, then detection speed is improved, but detection accuracy deteriorates due to false positives from adjacent vehicles, obstacles, and road markings
Solution Approach 1:
The patent segments the lane marking detection process into multiple stages: initial detection using color and edge information for speed, followed by verification using active contour models for accuracy. This segmentation allows the system to maintain high detection speed while eliminating false positives through subsequent verification steps.
Solution Approach 2:
The patent introduces active contour models as an intermediary verification mechanism between initial edge detection and final lane marking confirmation. This intermediary step filters out false detections from adjacent vehicles and obstacles while preserving true lane markings, resolving the accuracy-speed contradiction.
2Measurement precision
If active contour models are used to verify lane markings, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary detection using fast color and edge-based methods before applying computationally intensive active contour models. This preliminary action narrows down the search space and identifies candidate regions, allowing the active contour verification to focus only on potentially accurate detections rather than processing the entire image, thus reducing overall processing time.
Solution Approach 2:
The patent applies active contour models selectively to specific regions of interest identified by preliminary edge detection, rather than processing the entire image uniformly. This local application of the verification algorithm maintains high detection accuracy while significantly reducing the computational burden and processing time compared to global verification.
3Adaptability or versatility
If AR route guide lines are displayed using GPS data, then navigation functionality is provided, but realism deteriorates due to mismatches between actual lanes and displayed lines
Solution Approach 1:
The patent replaces GPS-based virtual route line generation with vision-based detection of actual lane markings. By substituting the mechanical/GPS-based system with an optical/vision-based system, the AR route guide lines are anchored to the真实 lane markings detected through active contour models, eliminating the position and orientation mismatches that occur with GPS alone and significantly improving visual realism.
Data Source
AI summary
There is provided a method for detecting a lane marking using a processor including acquiring a drive image captured by an image capturing device of a vehicle which is running, detecting an edge corresponding to a lane marking from the acquired drive image and generating an edge image based on the detected edge, detecting a linear component based on the detected edge and generating a linearly processed edge image based on the detected linear component, detecting a lane marking point corresponding to the lane marking using the generated edge image and the linearly processed edge image, and detecting the lane marking based on the detected lane marking point.