Camera Coverage Detection Using Pixel Brightness Analysis
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Solution Overview
Problem
Existing camera systems for motor vehicles face challenges in detecting a covered or masked state, particularly when the lens is obstructed by opaque or transparent objects, leading to false negatives due to noise or saturation issues, which can cause high-frequency image components and misinterpretation by algorithms.
Innovation Solution
A method that determines the number of pixels with specific brightness criteria within an image region to differentiate between opaque and transparent obstructions, using YUV color space brightness values and additional criteria to assess the camera's state, complementing high-pass filtering algorithms to accurately detect camera coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-pass filtering is used to detect camera coverage, then transparent obstructions can be detected, but opaque obstructions cause false negatives due to noise being misinterpreted as high-frequency components
Solution Approach 1:
The detection process is segmented into multiple independent analysis paths: one for detecting transparent obstructions using high-pass filtering, and another for detecting opaque obstructions using saturation analysis and brightness criteria. Each segment handles a specific type of obstruction independently, avoiding the interference that causes false negatives in a unified approach.
Solution Approach 2:
The invention changes the detection parameters based on the type of obstruction. For transparent obstructions, it uses high-pass filtering to detect high-frequency components. For opaque obstructions, it switches to analyzing pixel saturation and brightness values. This parameter adaptation allows accurate detection across different obstruction types without mutual interference.
2Reliability
If high-pass filtering is used to detect camera coverage, then the algorithm can identify contaminated states, but saturated pixels from bright light sources cause false edges and detection failures
Solution Approach 1:
The invention applies preliminary anti-action by detecting saturated pixels before they can interfere with the high-pass filtering process. By identifying pixels that have reached maximum brightness values and excluding them from analysis, the method prevents the formation of false edges that would otherwise lead to incorrect detection results.
Solution Approach 2:
The invention extracts and removes saturated pixels from the image data before applying high-pass filtering. By separating these problematic pixels from the rest of the image analysis, the method eliminates their harmful effect of creating false high-frequency components while preserving the detection capability for actual contaminants.
3Reliability
If the lens is covered by an opaque object, then no light enters the camera, but the noise signal is misinterpreted as high-frequency components indicating no coverage
Solution Approach 1:
The invention implements dynamic detection strategies that adapt to the observed image characteristics. When the image shows uniformly low brightness with noise patterns typical of opaque coverage, the system dynamically switches from high-pass filtering to a noise-based detection mode that correctly interprets the signal as indicating coverage rather than clear conditions.
Data Source
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AI summary
The invention relates to a method for recognizing a covered state of a camera (4) of a motor vehicle (1) by providing an image of an environmental region of the motor vehicle (1) by means of the camera (4), determining respective brightness values of pixels within at least one image region of the image, determining a number of pixels within the at least one image region, which satisfy a preset criterion with respect to the brightness values, and checking whether the camera (4) is covered or not covered depending on the number.