Traffic Sign Detection Using Color-Shape Candidate Regions
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Solution Overview
Problem
Existing driving assistance systems face challenges in efficiently identifying traffic signs in real-time due to excessive calculations, which reduces recognition rates and makes them prone to errors, especially when signs are damaged or contaminated.
Innovation Solution
A driving assistance apparatus and image processing method that identifies candidate areas for traffic signs using color and shape information, reducing computational load by dividing images into blocks and applying pixel information to a training model, while also considering environmental factors like illuminance and rainfall to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image area is searched for traffic signs, then the recognition rate is improved, but the computational load increases excessively
Solution Approach 1:
The patent divides the image processing task into multiple stages: first identifying candidate regions using color information, then further analyzing those regions using shape information, and finally recognizing traffic signs within the refined candidate areas. This segmentation approach reduces the search space from the entire image to specific candidate regions, significantly lowering computational load while maintaining recognition accuracy.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their likelihood of containing traffic signs. Candidate regions identified through color and shape analysis receive more intensive processing (pixel information extraction and training model application), while other regions are quickly discarded. This local quality approach optimizes computational resources by focusing detailed analysis only where needed.
2Productivity
If the computational load is reduced to enable real-time search, then the processing speed is improved, but the recognition rate decreases
Solution Approach 1:
The patent performs preliminary filtering using color information to identify candidate regions before conducting more intensive shape analysis and traffic sign recognition. This preliminary action eliminates large portions of the image that do not contain traffic signs, enabling real-time processing speed while preserving recognition accuracy by maintaining all potential traffic sign regions through the filtering stages.
Solution Approach 2:
The patent transitions from analyzing entire image regions to analyzing divided blocks within candidate regions. By dividing candidate areas into multiple blocks and extracting pixel information from these blocks, the system processes smaller units that are faster to compute while collectively covering the entire candidate region, thus improving processing speed without sacrificing recognition rate.
3Area of stationary object
If traditional image search methods are used, then comprehensive coverage is achieved, but the system becomes vulnerable to damage or contamination of signs
Solution Approach 1:
The patent divides candidate areas into multiple blocks and extracts pixel information from each block independently. This segmentation allows the system to analyze local characteristics of traffic signs even when parts are damaged or contaminated. By processing blocks separately and applying training models to multiple blocks within a candidate region, the system can identify traffic signs robustly even when some blocks are affected by damage or contamination.
Data Source
AI summary
Disclosed is a driving assistance apparatus which can be mounted on a vehicle. The driving assistance apparatus includes an image capturing part, and a processor configured to identify a specific color area in a peripheral image captured by the image capturing part as a first candidate area, identify an area including a specific shape in the first candidate area as a second candidate area, and identify an area of a traffic sign in the second candidate area.