Traffic Light Color Detection Using Spatial Interval Feature Maps
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Solution Overview
Problem
Conventional methods for detecting traffic light colors from camera images are prone to errors due to strong sunshine, reflections, or external disturbances, as they rely solely on luminance levels of circular regions without considering the arrangement patterns of color components.
Innovation Solution
An image processing apparatus that uses a controller to identify a signal region in a camera image, sets different spatial intervals for detectors to detect color components, generates feature maps based on these detections, and determines the light color of the traffic light based on the feature maps, rather than solely relying on luminance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only luminance levels of circular regions are compared to determine light color, then the detection process is simple, but the detection accuracy deteriorates due to strong sunshine, illuminations or strong-reflecting objects
Solution Approach 1:
The patent segments the traffic light detection process into multiple independent components: (1) detecting multiple color components (red, green, yellow) separately, (2) analyzing spatial intervals between these components, and (3) generating feature maps for each color component. This segmentation allows the system to evaluate multiple characteristics independently and combine them for more accurate light color determination, reducing susceptibility to external disturbances.
Solution Approach 2:
The patent transitions from one-dimensional luminance comparison to multi-dimensional analysis by incorporating spatial interval information as an additional dimension. Instead of only comparing brightness values, the system now considers both luminance levels and the spatial relationships between color components, creating a more robust detection framework that can distinguish true traffic light signals from false positives caused by environmental factors.
2Measurement precision
If multiple detectors with different spatial intervals are used to generate feature maps, then the light color detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal detection framework where the same detector structure and processing algorithm can identify multiple color components (red, green, yellow) simultaneously. The feature map generation process is designed to handle any color component type, making the system multi-functional rather than requiring separate dedicated systems for each color, thus managing complexity while maintaining high detection accuracy.
Solution Approach 2:
The system manages complexity by dynamically adjusting processing parameters such as spatial intervals and detection thresholds based on the specific detection scenario. Rather than using fixed complex structures, the algorithm adapts parameters like detector spacing and feature map resolution to optimize performance for different traffic light configurations and distances, reducing the need for overly complex hardware designs.
Data Source
AI summary
An image processing apparatus includes a controller that determines a light color of a traffic light from a camera image. The controller is configured to: (i) perform image recognition of the camera image to identify a signal region in which the traffic light exists in the camera image; (ii) set a plurality of different spatial intervals between detectors that detect pixels in the signal region having respective color components of respective lights included in the traffic light; (iii) generate a plurality of feature maps indicating a feature amount for an arrangement pattern of each of the respective color components based on detections of the signal region by the detectors using the plurality of different spatial intervals; and (iv) determine the light color of the traffic light based on the plurality of feature maps.