Section Line Recognition Using Multi-Camera Coordinate Transformation
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Solution Overview
Problem
Existing section line recognition devices face limitations in determining line type accuracy due to single-camera systems prone to non-detection or erroneous detections, and multi-camera systems struggling with lens contamination and varying line appearances based on vehicle distance.
Innovation Solution
A section line recognition device employing multiple cameras, a feature point extraction unit, coordinates transformation, camera determination, state transition analysis, and line type determination to accurately identify line types on a road surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used for section line recognition, then the device complexity is reduced, but the reliability of detection results deteriorates due to non-detection or erroneous detection
Solution Approach 1:
The patent combines multiple cameras (front camera, rear camera, and side cameras) to capture images of the road surface. By merging the detection results from multiple cameras, the system achieves higher reliability in section line recognition while maintaining manageable device complexity through integrated processing.
2Reliability
If multiple cameras are used to improve detection reliability, then the reliability of detection results is improved, but the device complexity increases
Solution Approach 1:
The patent divides the detection task among multiple cameras positioned at different locations (front, rear, sides). Each camera is responsible for capturing specific road sections, and the processing unit segments the overall detection task by selecting appropriate cameras based on vehicle position and road conditions, thereby managing complexity while maintaining reliability.
Solution Approach 2:
The processing unit acts as an intermediary that receives images from multiple cameras, selects appropriate images based on detection needs, and integrates the results. This intermediary component coordinates the multiple cameras without requiring complex direct interactions between them, thus improving reliability while controlling system complexity.
3Measurement precision
If multiple cameras are used to determine line type, then the measurement precision is improved, but the difficulty of detecting and measuring increases due to lens contamination and varying line appearances
Solution Approach 1:
The patent changes the parameter of camera selection based on detection conditions. When lens contamination is detected or when line appearance varies due to distance, the system switches between different cameras (front, rear, side) to maintain measurement precision. This dynamic parameter adjustment allows the system to overcome detection difficulties while maintaining high precision in line type recognition.
Data Source
AI summary
A section line recognition device is realized which can determine a line type of a section line in a road surface with high accuracy. In step 301, a feature amount is extracted from an image captured by each camera. This step corresponds to a process of a feature point extraction unit. In the next step 302, the extracted feature amount is transformed into bird's-eye view coordinates which is common coordinates. This step corresponds to a process of a coordinates transformation unit. Next, in step 303, a camera to be selected is determined. This step is a process of a camera determination unit. Next, in step 304, the state transition at the appearance position of the feature point on the bird's-eye view coordinates is determined using feature point coordinates of the camera selected in step 303. This step is a process of a state transition determination unit. Finally, in step 305, the line type is determined. This step is a process of a line type determination unit.