Binocular Camera Imaging Environment Evaluation via Disparity Projection
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Solution Overview
Problem
Existing binocular camera systems in the automatic driving field face challenges in evaluating imaging environments in real time due to difficulties in detecting brightness values, which affects imaging quality and timeliness.
Innovation Solution
A method involving the acquisition of a disparity information matrix and pixel coordinates of a vanishing point, followed by matrix partition and projection to obtain a disparity projection image, and then fitting a road surface model-based statistic model to evaluate the imaging environment using a predetermined threshold, allowing for real-time assessment of imaging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an illuminometer is used to measure brightness value, then measurement precision is improved, but real-time monitoring capability deteriorates
Solution Approach 1:
The patent replaces the mechanical illuminometer measurement system with a computational method that uses binocular camera disparity information and vanishing point detection to infer imaging environment quality. This substitution eliminates the need for physical brightness sensors while enabling real-time evaluation through image processing algorithms.
Solution Approach 2:
The patent introduces disparity information and vanishing point coordinates as intermediary parameters to indirectly assess imaging environment quality. Instead of directly measuring brightness, the system uses these intermediate computational features that can be derived from binocular vision data to evaluate whether the imaging environment is suitable.
2Measurement precision
If binocular camera systems are used for visual detection, then imaging quality assessment capability is improved, but real-time environment evaluation capability deteriorates
Solution Approach 1:
The patent extracts only the essential features (disparity information and vanishing point coordinates) from the full binocular camera data stream to evaluate imaging environment quality. By taking out only the necessary computational elements rather than processing complete images, the system achieves real-time evaluation capability while maintaining assessment accuracy.
Solution Approach 2:
The patent segments the imaging environment evaluation task into distinct computational steps: acquiring disparity information, detecting vanishing points, and evaluating environment quality based on these features. This segmentation allows for efficient processing and real-time performance.
Data Source
AI summary
Provided is a road surface information-based imaging environment evaluation method, an imaging environment evaluation device, an imaging environment evaluation system, and a storage medium. The imaging environment evaluation method includes: acquiring a disparity information matrix and pixel coordinates of a vanishing point in the disparity information matrix; subjecting the disparity information matrix to matrix partition and projection in accordance with the pixel coordinates of the vanishing point so as to acquire a disparity projection image; acquiring a road surface model-based statistic model in accordance with the disparity projection image; and acquiring an evaluation result of a current imaging environment in accordance with a relationship between the statistic model and a predetermined threshold.