Road Marking Detection Using Learned Feature Templates
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Solution Overview
Problem
Conventional road marking detection methods are inefficient, particularly in real-time applications, as they rely on lane detection techniques that require extensive hand-coding, are slow, and struggle with complex markings and varying lighting conditions, especially when lane markings are absent or multiple disconnected components are present.
Innovation Solution
A feature-based road marking detection system that learns templates from training images using a template learning module, which rectifies images, detects regions of interest, and extracts feature vectors to build road marking templates, enabling efficient detection and matching in real-time without the need for special hardware like GPUs, and scales well with the number of marking types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional lane detection techniques with edges and regions are used for road marking detection, then road markings can be detected, but the method requires extensive hand-coding for each marking type and does not scale well
Solution Approach 1:
The patent uses template matching where pre-defined road marking templates are copied and matched against detected regions. Instead of hand-coding detection logic for each marking type, the system creates reusable templates that can be matched automatically, enabling scalability to multiple marking types without proportional increase in coding complexity
Solution Approach 2:
The patent implements a universal detection framework that handles multiple road marking types (arrows, text, symbols) through a single integrated system. The template-based approach allows one detection mechanism to serve multiple functions across different marking types, eliminating the need for separate hand-coded detectors for each type
2Productivity
If conventional methods detect road markings using scan lines and horizontal extent computation, then markings can be classified, but the detection is too slow for real-time applications
Solution Approach 1:
The patent performs preliminary actions by pre-processing images to detect candidate regions of interest before template matching. Regions are pre-filtered based on basic characteristics (size, shape, location) to identify only potential road markings. This preliminary filtering reduces the number of regions requiring detailed template matching, significantly speeding up real-time detection
Solution Approach 2:
The patent segments the detection process into distinct stages: candidate region detection, template matching, and classification. By dividing the overall detection task into smaller sequential segments, each stage can be optimized independently, improving overall processing speed for real-time applications
3Adaptability or versatility
If eigenspace technique is used for template matching with multiple generated images, then ideal templates under different conditions can be matched, but the method relies on proper cropping and centering which is inefficient for real-time operation
Solution Approach 1:
The patent creates multiple templates by varying parameters such as illumination conditions, scale, and blur levels. Instead of requiring perfect cropping and centering, the system generates templates with different parameter values to account for variations in real-world conditions, making the detection more robust and easier to operate
Solution Approach 2:
The patent prepares multiple templates in advance that cushion against variations in illumination, scale, and blur. By having pre-generated templates that account for these variations, the system is buffered against the challenges of real-time operation where perfect image conditions cannot be guaranteed
4Reliability
If conventional methods are used when lane markings are absent or multiple disconnected components are present, then detection may still be attempted, but performance deteriorates significantly
Solution Approach 1:
The patent enables the detection system to serve itself by using detected road markings to guide further detection. The system detects individual marking components and uses their spatial relationships to identify complete markings even when lane markings are absent or components are disconnected, improving reliability in challenging conditions
Data Source
AI summary
A system and method are disclosed for detecting road marking in a video using learned road marking templates. The system comprises a template learning module configured to learn the feature-based road marking templates from a set of training images. The template learning module is configured to rectify each training image, detect multiple regions of interest, and for each detected region of interest, detect multiple key points. The template learning module extracts feature vectors for the detected key points and builds the road marking templates from the feature vectors. The system also includes a road marking detection module for detecting road markings in a video at runtime using the learned road marking templates. During runtime, these templates are matched using a two-step process of first selecting promising feature matches and subsequently performing a structural matching to account for the shape of the road markings.