Aerial Image Tone Correction for Road Marking Detection
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Solution Overview
Problem
Aerial images with shadows from buildings complicate the detection of road markings due to significant luminance differences between shadowed and unshadowed regions, making it difficult to accurately identify road features for high-precision map generation, especially in automated driving systems.
Innovation Solution
An apparatus and method that classify shadowed and unshadowed regions in aerial images, determine a tone correction factor to reduce luminance differences, and convert the image from RGB color space to a predetermined color space, such as HLS or HSV, to minimize contrast and facilitate road marking detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aerial images are used for road marking detection, then map information can be obtained, but shadow regions create large luminance differences that reduce detection accuracy
Solution Approach 1:
The patent converts the image from RGB color space to a different color space (such as LAB or HSV) where the luminance or lightness component is separated from color information. This parameter change in color space representation allows the shadowed and unshadowed regions to have more comparable luminance values, reducing the harmful luminance difference caused by shadows while preserving road marking detectability.
2Reliability
If tone correction is applied to reduce luminance differences, then shadow effects are minimized, but additional processing steps increase system complexity
Solution Approach 1:
The patent performs color space conversion as a preliminary action before road marking detection. By transforming the image into a color space with separated luminance and color components, the shadow-induced luminance variation is addressed in advance, allowing subsequent detection algorithms to work with more uniform lighting conditions without requiring complex real-time tone correction during detection.
Data Source
AI summary
An apparatus for image conversion includes a processor configured to classify a reference region representing a predetermined feature into a shadowed region and an unshadowed region, the reference region being in an aerial image represented in RGB color space; determine a tone correction factor so that a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region in the aerial image represented in predetermined color space to which the color space of the aerial image is converted from RGB color space is less than a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region represented in RGB color space; correct tones of the aerial image with the tone correction factor; and convert the color space of the aerial image from RGB color space to the predetermined color space to generate a color conversion image.