Surround-View Image Processing for Parking Space Detection Precision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current in-vehicle surround-view systems face challenges in achieving precise detection of parking spaces and obstacles due to inconsistent precision across different regions, leading to reduced parking success rates and potential traffic accidents.
Innovation Solution
A data processing method that preconfigures multiple homography matrices based on specific parking states, allowing for dynamic image processing and ultrasonic radar configurations to enhance detection precision and location accuracy, thereby improving parking success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single homography matrix is used for all regions in the in-vehicle surround view, then the device complexity is reduced, but the detection precision and location accuracy for different parking states deteriorate
Solution Approach 1:
The patent divides the in-vehicle surround view into multiple feature regions based on distance from the vehicle body (first feature region for far-end targets, second feature region for near-end targets). Each region is processed with a dedicated homography matrix optimized for its specific distance characteristics, thereby improving detection precision without requiring a single complex matrix to handle all scenarios
Solution Approach 2:
The patent dynamically selects and switches between different homography matrices based on the detected parking state and target location. The system determines which feature region contains the to-be-detected target and applies the corresponding homography matrix, enabling adaptive precision optimization for different parking scenarios
2Measurement precision
If multiple homography matrices are used for different parking states, then the detection precision and location accuracy are improved, but the device complexity increases
Solution Approach 1:
The patent applies different homography matrices to different feature regions based on their specific requirements. The first homography matrix is optimized for the first feature region (far-end targets) and the second homography matrix for the second feature region (near-end targets), ensuring each region receives the most appropriate transformation parameters for its detection needs
Solution Approach 2:
The patent changes the homography matrix parameters dynamically based on the parking state and target location. By switching between different homography matrices corresponding to different parking states, the system optimizes detection precision without requiring a single overly complex matrix configuration
3Reliability
If region-specific parameter constraint is applied to improve detection precision, then the parking success rate is improved, but the processing time and computational overhead increase
Solution Approach 1:
The patent applies parameter constraint and homography matrix processing only to the specific feature region where the to-be-detected target is located, rather than processing the entire image with multiple matrices. This partial action approach improves detection precision for the relevant region while minimizing unnecessary computational overhead and processing time
Data Source
AI summary
Example data processing methods and apparatus are provided. One example method includes obtaining an image captured by an in-vehicle camera. A to-be-detected target in the image is determined. A feature region corresponding to the to-be-detected target in the image is further determined based on a location of the to-be-detected target in the image. A first parking state is determined based on the image and wheel speedometer information. A first homography matrix corresponding to the first parking state is determined from a prestored homography matrix set, where different parking states correspond to different homography matrices. Image information of the feature region is processed based on the first homography matrix to obtain a detection result.