Vehicular Image Processing for Wet Road Adaptation
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Solution Overview
Problem
Image processing systems in vehicles face challenges in adapting to varying road surface conditions, particularly when the road is wet, as existing systems struggle to accurately determine moisture levels and adjust imaging conditions and processing tasks accordingly.
Innovation Solution
An image processing apparatus that generates a histogram based on luminance values of road-surface pixels to identify separate local crests, which indicates wet conditions, and adjusts imaging conditions and processing tasks to suit the specific situation, thereby improving image processing accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic white balancing is performed using conventional methods, then processing speed is maintained, but processing accuracy deteriorates under wet road conditions
Solution Approach 1:
The system dynamically adjusts the white balancing processing based on detected road conditions. When wet road conditions are detected through histogram analysis showing specific luminance distribution patterns, the system switches to a modified white balancing algorithm that accounts for the reflective properties of wet surfaces, thereby maintaining processing accuracy across varying conditions.
Solution Approach 2:
The system changes processing parameters based on road condition detection. By analyzing luminance value distributions and identifying characteristic patterns of wet roads, the system modifies white balancing parameters such as reference white points and adjustment weights to compensate for the specific optical properties of wet surfaces.
2Reliability
If imaging conditions are fixed, then device complexity is reduced, but processing reliability deteriorates under varying road conditions
Solution Approach 1:
The system performs self-adjustment by automatically detecting road conditions through histogram analysis of captured images and autonomously selecting appropriate processing parameters. This self-service mechanism eliminates the need for manual intervention or complex external control systems while maintaining high processing reliability across different road conditions.
Solution Approach 2:
The system implements a feedback loop where processing results are continuously evaluated and used to adjust subsequent processing parameters. By monitoring the effectiveness of white balancing and other image processing operations, the system refines its parameter selection to maintain optimal performance under varying road conditions.
3Measurement precision
If conventional image processing tasks are used, then processing speed is maintained, but measurement precision deteriorates in low-light wet conditions
Solution Approach 1:
The system segments the image processing pipeline into distinct stages: initial quick assessment using conventional fast algorithms, condition detection through histogram analysis, and selective application of enhanced processing only to affected regions or frames. This segmentation allows the system to maintain high speed during normal conditions while applying precision-enhancing operations only when and where needed.
Solution Approach 2:
The system applies enhanced processing operations selectively rather than universally. By identifying specific regions or frames that require improved processing under wet or low-light conditions and applying enhanced algorithms only to those cases, the system achieves higher measurement precision where needed while minimizing the overall processing overhead.
Data Source
AI summary
In an image processing apparatus, an image obtaining unit obtains, from a vehicular camera, an image captured by the vehicular camera based on a predetermined imaging condition. An image processing unit executes an image-processing task of the image. A histogram generation unit generates a histogram based on the luminance values of the pixels included in the road-surface region in the image. The histogram graphically represents a frequency of each of the luminance values of the pixels included in the road-surface region in the image. A histogram determination unit determines whether the histogram has first and second separate crests. A change unit changes, upon determination that the histogram has first and second separate crests, at least one of1. The imaging condition of the vehicular camera2. The image-processing task to be executed by the image processing unit.