Image Conversion Using Region Segmentation for Road Profile Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated driving systems face challenges in accurately converting 2D image coordinates to 3D coordinates, especially when road inclinations change, leading to distortion and reduced accuracy in detecting road boundaries and distances to vehicles ahead.
Innovation Solution
The system segments the input image into region images based on inclination levels, calculates different homography matrices for each region, and determines vanishing points to generate accurate road profile data by converting 2D coordinates to 3D coordinates using these matrices, minimizing distortion and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single homography matrix is used to convert 2D image coordinates to 3D coordinates, then the conversion process is simple, but accuracy deteriorates when road inclinations change causing distortion
Solution Approach 1:
The input image is divided into multiple region images based on inclination levels. Each region image corresponds to a specific road inclination range, allowing the system to apply appropriate conversion parameters for each segment. This segmentation enables accurate coordinate conversion across varying road inclinations without requiring a single complex global model.
Solution Approach 2:
Different homography matrices are calculated and applied to different region images based on their specific inclination characteristics. Each region has its own optimized conversion parameters tailored to local road conditions, improving overall conversion accuracy while maintaining manageable complexity through localized processing.
2Measurement precision
If region-based processing with multiple homography matrices is used, then coordinate conversion accuracy improves, but device complexity increases
Solution Approach 1:
The system pre-calculates and stores multiple homography matrices corresponding to different inclination levels before actual coordinate conversion is needed. This preliminary preparation allows the processing system to simply look up and apply the appropriate matrix based on the detected inclination level, significantly reducing real-time computational complexity while maintaining high accuracy.
Solution Approach 2:
The system changes the conversion parameters (homography matrices) based on the detected inclination level of each region. By adapting the conversion parameters to match local road conditions, the system achieves high accuracy without requiring a single overly complex universal conversion model.
3Measurement precision
If vanishing points are determined for each region image, then road profile data accuracy improves, but measurement and detection difficulty increases
Solution Approach 1:
By dividing the image into multiple region images with smaller field-of-view ranges, the system reduces the complexity of vanishing point detection within each region. The detection algorithm operates on smaller, more localized areas, making it easier to identify road boundaries and calculate accurate vanishing points for each segment.
Solution Approach 2:
The system adjusts detection parameters and algorithms based on the specific characteristics of each region image. By tailoring the detection approach to local conditions within each region, the system improves measurement accuracy while managing detection complexity through localized optimization.
Data Source
AI summary
Provided are an apparatus and a method for converting an image. The apparatus includes an image sensor configured to obtain an image of a road, and a processor. The processor is configured to segment an input image into a plurality of region images, determine a vanishing point corresponding to each of the plurality of region images, obtain a translation relation for converting two-dimensional (2D) coordinates of a point in a region image among the plurality of region images into three-dimensional (3D) coordinates, based on a vanishing point of the region image, and generate road profile data based on translation relations of the plurality of region images.