Lane Detection via Edge Style Classification and Seed Region Identification
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Solution Overview
Problem
Conventional lane detection algorithms struggle to effectively detect lane markers in situations with no obvious gradient change, light reflection, or obstacles, often failing to provide accurate two-dimensional information, leading to inefficiencies and missed lane detections.
Innovation Solution
A lane departure warning system that classifies image pixels into edge styles using gradient values and thresholds, identifies road marker seed regions, and determines intensity thresholds to detect and extend lane markers through a straight line equation, simplifying the detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional edge detection algorithms are used to detect lane markers, then the detection process is simple, but the detection accuracy deteriorates in cases with no obvious gradient change, light reflection, or obstacles
Solution Approach 1:
The patent segments the image processing task into multiple specialized modules: edge style classification module that categorizes pixels by edge characteristics, road marker seed region detection module that identifies potential lane marker areas, and lane detection module that performs final lane determination. This segmentation allows each module to specialize in specific aspects, improving overall detection accuracy while managing complexity through modular design.
Solution Approach 2:
The patent applies local quality by classifying different pixels into different edge styles (horizontal edge, vertical edge, diagonal edge, soft edge) based on their local gradient characteristics. Each pixel is treated according to its specific edge style rather than applying a uniform detection method, which improves detection accuracy for diverse lane marker conditions including those with light reflection or obstacles.
2Device complexity
If pixel-based image processing technology is used for lane detection, then the basic detection capability is achieved, but two-dimensional information cannot be obtained without complicated procedures
Solution Approach 1:
The patent transitions from traditional one-dimensional pixel-based detection to two-dimensional region-based detection by defining road marker seed regions and detecting lanes across multiple sub-boxes. The lane detection module uses straight line equations and extends local lanes to obtain complete lane information, achieving comprehensive two-dimensional lane representation without excessive procedural complexity.
3Productivity
If Region Of Interest (ROI) strategy is used to improve execution efficiency, then processing speed is improved, but lanes in non-ROI areas cannot be detected
Solution Approach 1:
The patent creates a universal lane detection system that can detect lanes anywhere in the image by dividing the entire image into multiple sub-boxes and performing detection in each sub-box independently. The system identifies road marker seed regions across the full image area and extends local lanes to cover the complete lane, ensuring both high execution efficiency through localized processing and comprehensive adaptability to detect lanes in any region.
4Measurement precision
If conventional edge detection is used for wide lane markers, then edge pixels can be detected, but correct lane inference becomes difficult
Solution Approach 1:
The patent extracts the essential line features from detected edge pixels by performing line feature evaluation on pixels of the same edge style within sub-boxes. The lane detection module then uses straight line equations to fit the extracted features and extends local lanes to obtain complete lane information, simplifying the inference process while maintaining accuracy for wide lane markers.
Data Source
AI summary
Provided is a lane departure warning system and method. The lane departure warning system includes an edge style classification map capture module, a road marker seed region detection module, a lane detection module, and a lane departure warning module, which can detect, by using an edge style classification map, and through road marker seed region detection and lane detection, lane edge pixels and identify real road marker edge pixels under a circumstance that there is no obvious gradient change or light is reflected or an obstacle exists, thereby determining a complete lane, and effectively finding a Region Of Interest (ROI) to simplify a procedure of lane detection.