Hue-Sensitive Pixel Array for Non-White Lane Boundary Detection
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Solution Overview
Problem
Existing lane detection systems using grayscale cameras struggle to identify non-white lane boundaries, such as yellow and blue markings, as they are indistinguishable from the road surface, leading to poor detection accuracy.
Innovation Solution
A method employing a two-dimensional array of image capturing pixels with a first set independent of hue and a second set sensitive to a limited range of hues, combined using defined rules to enhance edge detection, allowing for the identification of both white and non-white lane boundaries by producing a combined image that highlights dominant edge features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a grayscale imager is used to capture highway images, then the device complexity is reduced and manufacturing cost is lowered, but the detection precision of non-white lane boundaries deteriorates because yellow and blue markings become indistinguishable from the road surface
Solution Approach 1:
The pixel array is segmented into two distinct sets: first set pixels that capture grayscale information and second set pixels that capture hue information. This segmentation allows the system to process different types of visual information separately and combine them, resolving the contradiction by maintaining simple grayscale capture while adding targeted color detection capability only where needed.
Solution Approach 2:
Different regions of the pixel array have different functional qualities - the first set of pixels is optimized for grayscale detection while the second set is optimized for hue detection. This local differentiation allows the system to apply the right type of detection (grayscale or hue-based) to different parts of the image, improving lane boundary detection without requiring the entire system to be complex.
2Device complexity
If only grayscale image processing is used, then the processing complexity is reduced, but the detection reliability of lane boundaries deteriorates when markings have similar lightness to the road surface
Solution Approach 1:
A combined image is created as an intermediary representation that integrates both grayscale intensity information and hue-based edge information. This combined image serves as a mediator that preserves the simplicity of grayscale processing while incorporating the reliability benefits of hue-based detection, allowing the system to achieve high detection reliability without requiring complex multi-stream processing.
3Measurement precision
If a two-dimensional array with hue-sensitive pixels is used, then the detection precision of non-white lane boundaries is improved, but the device complexity increases due to the need for multiple pixel sets with different sensitivity characteristics
Solution Approach 1:
The pixel array is segmented into two distinct sets: first set pixels that capture grayscale information and second set pixels that capture hue information. This segmentation allows the system to process different types of visual information separately and combine them, resolving the contradiction by maintaining simple grayscale capture while adding targeted color detection capability only where needed.
Solution Approach 2:
The imaging device achieves multi-functionality by combining grayscale and hue detection capabilities in a single pixel array. The same physical device can detect both white lane boundaries (using grayscale information from the first set of pixels) and non-white lane boundaries (using hue information from the second set of pixels), eliminating the need for separate imaging devices for different detection tasks.
4Measurement precision
If hue-based edge detection is applied, then the detection accuracy of yellow and blue lane markings is improved, but the processing time increases due to additional image processing steps
Solution Approach 1:
Hue-based edge detection is performed preliminarily on the second set of pixels to identify potential lane boundary locations before combining with grayscale information. By performing hue-based detection in advance on a dedicated pixel set, the system can quickly identify regions of interest and then apply more sophisticated processing only where needed, reducing overall processing time while maintaining high detection accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively increases the likelihood of detecting lane boundaries by highlighting edges in both grayscale and hue images, improving detection accuracy for non-white markings, such as yellow lines on gray roads.
Implementation Method 1
a first set of pixels located at spaced coordinates (XY) which each produce an output signal whose value is substantially independent of the hue of the corresponding portion of the scene captured in the image
Implementation Method 2
a second set of pixels located at spaced locations which each produce an output signal having a value dependent on a limited range of hue(s) of the corresponding portion of the scene captured in the image
Data Source
AI summary
A method of processing an image includes a region of a highway in front of the vehicle captured using an imager having a two dimensional array of image capturing pixels. The array includes a first set of pixels substantially independent of hue and a second set of pixels dependent of a limited range of hues. The method further includes the steps of producing a first image where each pixel is assigned a value derived from the first set of pixels and producing a second image where each pixel is assigned a value derived from the first set of pixels and the second set of pixels. The method includes identifying-for at least one pixel in the first image a first intensity change value indicative of the difference between that pixel and at least one adjacent pixel identifying for a corresponding pixel in the second image a second intensity change value indicative of the difference between that pixel and at least one adjacent pixel. A combined image is formed using a defined set of combination rules to assign a value to the corresponding pixel in the combined image which depends on the first and second identified intensity change values for that corresponding pixel.