Vehicular Image Processing for Lane Mark Detection
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Solution Overview
Problem
Conventional vehicular image processing devices struggle to accurately detect lane marks on roads due to variations in road surface luminance and color, leading to errors in differentiating between road surface areas and non-road surface areas, especially after road repairs or changes in road surface color over time.
Innovation Solution
A vehicular image processing device that sets a kernel size for image smoothing, calculates pixel value variations, and replaces pixel values based on predefined thresholds to maintain lane mark pixel values while adjusting for local repairs and shadows, allowing precise detection of lane marks by maintaining lane mark pixel values and replacing those of road surface and partial areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the color of the lower portion of the window is used as the reference color for road surface area, then the road surface area can be differentiated from the non-road surface area, but the detection accuracy deteriorates when the road surface color varies due to repairs, shadows, or time passage
Solution Approach 1:
The patent changes the reference color determination method from using a fixed lower portion color to dynamically calculating a reference color based on the median color of the differentiated road surface area. This parameter change allows the system to adapt to color variations in the road surface caused by repairs, shadows, or aging, thereby maintaining detection accuracy while improving reliability.
Solution Approach 2:
The patent implements a feedback mechanism where the reference color is recalculated based on the actual color distribution in the differentiated road surface area. By using the median color of the road surface area as the reference, the system continuously adjusts to reflect the current state of the road surface, compensating for variations due to repairs, shadows, or time passage.
2Measurement precision
If a fixed window position is used for image processing, then the processing method is simple, but the detection accuracy deteriorates when road surface color varies across different areas
Solution Approach 1:
The patent transitions from a fixed window position approach to a dynamic window position approach. The window position is automatically adjusted based on the detected lane mark position and the calculated reference color. This dynamic adjustment allows the system to maintain optimal processing parameters regardless of road surface color variations, improving detection accuracy without requiring complex manual intervention.
Solution Approach 2:
The patent segments the image processing into distinct steps: color differentiation, reference color calculation, and lane mark detection. By segmenting the process and using the median color of the differentiated area as the reference, the system can handle color variations more effectively while maintaining processing simplicity through automated segmentation-based approach.
Data Source
AI summary
A vehicular image processing device is provided with a kernel setting unit which sets a plurality of smoothing kernels which have a width supposed to be between the width of the lane mark and that of the road to the image acquired from a photographing unit, a smoothing unit which smoothes the acquired image by filtering using the set smoothing kernels, a variation degree calculating unit which calculates the variation degree of the pixel value of each pixel in the acquired image with respect to the smoothed image, and a pixel value replacing unit which replaces the pixel value of a pixel which is of the acquired image and has the variation degree not greater than a predefined value with a specific value.