Vehicle Camera Blindness Detection via Dynamic Thresholding
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Solution Overview
Problem
Existing image recording devices in vehicles face challenges in accurately detecting blindness, which can lead to malfunction of functions relying on camera images, due to factors like extreme dirtiness or defects, especially in scenes with significant brightness differences.
Innovation Solution
A method that analyzes images by determining brightness information, setting threshold values based on exposure characteristics, and calculating blindness probabilities for different regions, using relinearized histograms to adapt sensitivity and incorporate visibility information for improved blindness detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed threshold value is used for blindness detection, then the detection process is simple, but the detection accuracy deteriorates in scenes with significant brightness differences
Solution Approach 1:
The patent implements dynamic threshold adjustment by calculating a threshold value for each image based on brightness information and exposure characteristics. Instead of using a fixed threshold, the system adapts the threshold dynamically to match the specific lighting conditions of each captured image, thereby maintaining high detection accuracy across varying brightness scenarios while managing complexity through automated calculations.
Solution Approach 2:
The patent changes the parameter of the threshold value from a fixed constant to a variable that depends on brightness information and exposure characteristics. By calculating the threshold based on the histogram and exposure settings of each image, the system transforms the threshold into an adaptive parameter that automatically adjusts to different lighting conditions, resolving the contradiction between simplicity and accuracy.
2Area of stationary object
If blindness detection is performed over the entire image, then the detection coverage is complete, but the detection accuracy deteriorates in scenes with great brightness differences in parts of the image
Solution Approach 1:
The patent divides the image into multiple regions and performs blindness detection separately for each region. This segmentation allows the system to handle different brightness conditions in different parts of the image independently, improving accuracy by applying region-specific threshold values based on local brightness characteristics while maintaining complete coverage through comprehensive region-based analysis.
3Measurement precision
If the sensitivity of blindness detection is increased, then the detection precision is improved, but the number of false positives increases
Solution Approach 1:
The patent adjusts the sensitivity parameter dynamically by calculating threshold values based on brightness information and exposure characteristics for each image. This parameter adaptation allows the system to optimize detection precision for each specific scene while automatically adjusting to avoid false positives caused by inappropriate sensitivity levels in different lighting conditions.
Data Source
AI summary
In a method for analyzing an image of an image recording device for a vehicle, a piece of brightness information of the image is determined, taking an exposure characteristic of the image recording device into account, and a threshold value is set as a function of the piece of brightness information. An image structure of the image is furthermore determined, and a blindness value for the image is determined on the basis of the image structure, using the threshold value.