Traffic Sign Recognition Using 3D Point-Group Data and Color Thresholds
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Solution Overview
Problem
Existing traffic sign recognition systems struggle to accurately differentiate between traffic signs displayed on road signs and guide signs, particularly when using camera-based detection, leading to potential misclassification of traffic regulation signs.
Innovation Solution
A traffic sign recognition device and method that utilizes three-dimensional point group data and camera images to estimate the relative position and size of a traffic sign candidate, calculates the percentage of specific color components, and applies thresholds to distinguish between road and guide signs based on their distinct features, including height and color composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If camera-based detection is used to recognize traffic signs, then the detection speed and coverage are improved, but the accuracy of differentiating between road signs and guide signs deteriorates
Solution Approach 1:
The patent transitions from two-dimensional camera images to three-dimensional point group data to resolve the recognition ambiguity. By utilizing depth information and spatial coordinates from LiDAR or other 3D sensors, the system can accurately determine the relative position, height, and distance of traffic signs, enabling differentiation between road signs and guide signs that appear similar in 2D images but have distinct spatial characteristics.
Solution Approach 2:
The patent introduces multiple parameters beyond simple image recognition, including relative position, height, distance, and color component percentages. By analyzing these multiple parameters simultaneously and comparing them against threshold values, the system achieves more accurate classification of traffic signs while maintaining fast detection speeds through efficient computational methods.
2Device complexity
If only camera images are used for traffic sign recognition, then the device complexity is reduced, but the reliability of recognition deteriorates
Solution Approach 1:
The patent combines camera images with three-dimensional point group data from LiDAR or other depth sensors to create a multi-modal recognition system. This integration allows the system to leverage both the visual information from cameras and the spatial depth information from 3D sensors, significantly improving recognition reliability while keeping the overall device complexity manageable through coordinated processing of multiple data sources.
Solution Approach 2:
The patent introduces a processor as an intermediary that fuses camera image data with three-dimensional point group data. This intermediary component performs coordinate transformations, calculates relative positions and heights, and integrates information from both sensors to produce reliable traffic sign recognition results, bridging the gap between simple imaging and complex multi-sensor fusion.
3Measurement precision
If the system uses multiple parameters (position, height, color) for recognition, then the recognition accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent implements a threshold-based filtering approach where color component percentages and spatial parameters are compared against predetermined thresholds. This partial action approach allows the system to quickly eliminate non-matching candidates without performing exhaustive analysis on all parameters for all detected objects, thereby maintaining high recognition accuracy while controlling computational complexity through selective processing.
Data Source
AI summary
A traffic sign recognition device includes a storage device configured to store a camera image from a movable body and pieces of three-dimensional point group data, and a processor. The processor is configured to: estimate a relative position of the traffic sign candidate to the movable body; specify a set of three-dimensional point group data; and specify an image region of an object corresponding to a set region indicative of a region where the set is specified, the object including the traffic sign candidate. The processor is configured to calculate a percentage of a predetermined color component constituting a guide sign among color components constituting an image of the object. In a case where the percentage of the predetermined color component is equal to or more than a threshold, the processor recognizes the object including the traffic sign candidate to be the guide sign.