Traffic Light Image Composition for Robust Signal Recognition
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying traffic lights due to varying weather and environmental conditions, which can disable autonomous driving.
Innovation Solution
A processor-implemented method and apparatus that separates foreground and background in traffic light images using a color separator, trains a color transformation matrix to estimate foreground color, and uses a position estimator to generate a target image by composing foreground color and signal mask, enhancing the accuracy of traffic light identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic light detection methods are used, then the system is simple to implement, but the accuracy deteriorates under varying weather and environmental conditions
Solution Approach 1:
The patent segments the traffic light image into foreground (traffic light) and background components using color separator techniques. This segmentation allows independent processing of each component, improving identification accuracy by focusing computational resources on the traffic light region while maintaining system manageability through modular processing stages.
Solution Approach 2:
The patent introduces a color transformation matrix as an intermediary that maps background color values to foreground color values. This intermediary component enables accurate color estimation of the traffic light by leveraging the relationship between background and foreground colors, thereby improving identification accuracy without requiring direct complex analysis of the traffic light itself.
2Measurement precision
If color transformation matrix training is performed, then the color estimation accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the color transformation matrix using a dataset of traffic light images before actual operation. This pre-computed matrix can be directly applied to new images without retraining, significantly reducing inference time while maintaining high color estimation accuracy. The training is performed once in advance, and the results are stored for rapid deployment.
3Measurement precision
If position estimation using brightness values is applied, then the center position detection accuracy improves, but the sensitivity to lighting conditions worsens
Solution Approach 1:
The patent applies parameter changes by transforming the position estimation approach from direct brightness value analysis to color-based estimation using the trained color transformation matrix. This parameter transformation makes the position detection less sensitive to lighting conditions, as color information remains relatively stable across different illumination levels compared to raw brightness values.
Data Source
AI summary
A method and apparatus for composing a traffic light image are provided, where the method includes separating a foreground and a background of each of the one or more actual images using a color separator, training a color transformation matrix configured to estimate a color value of the foreground, training a position estimator configured to estimate a position of a center of the foreground from a brightness value of each of the one or more actual images, and generating a target image based on inputting an input image to the position estimator and the color separator.