Low-Light Camera Occlusion Detection via RGB Channel Ratios
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Solution Overview
Problem
Autonomous vehicles face challenges in determining whether a camera is occluded, especially in low ambient lighting conditions, which can lead to critical decision-making errors in real-time scenarios.
Innovation Solution
A method involving the analysis of output values from red, green, and blue pixels to determine chromaticity ratios and differences, using equations such as G=(Gr+Gb)/2 and r=R/(R+B+G, to differentiate between occlusion and capturing a dark scene, with threshold values based on image gain, enabling real-time detection and potential remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If camera captures images in low ambient lighting conditions, then the camera can operate in nighttime or dimly lit environments, but the ability to accurately detect occlusion is degraded
Solution Approach 1:
The patent utilizes chromaticity analysis by comparing the relative output values of red, green, and blue pixels. In low-light conditions, occluded cameras exhibit distinct chromaticity patterns compared to non-occluded cameras. By analyzing the ratios and differences between color channel outputs (R/(R+B+G), G/(R+B+G), B/(R+B+G)), the system can detect occlusion even when overall illumination is poor, thus resolving the contradiction between operating in low light and maintaining detection accuracy.
2Reliability
If the camera lens is completely or partially occluded, then the camera cannot capture useful images, but traditional detection methods fail in low-light conditions
Solution Approach 1:
The patent replaces traditional mechanical or visual inspection methods for detecting occlusion with an optical analysis approach. Instead of physically checking the lens or relying on visual confirmation (which fails in low light), the system substitutes a chromaticity-based detection algorithm that analyzes the color distribution patterns in captured images. This substitution enables reliable occlusion detection regardless of ambient lighting conditions, thus resolving the contradiction between reliability and detection difficulty.
Solution Approach 2:
The patent changes the detection parameter from overall image brightness or visual inspection to chromaticity ratios and color channel comparisons. By using parameters such as R/(R+B+G), G/(R+B+G), and B/(R+B+G) along with gain-adjusted thresholds, the system can detect occlusion states through subtle color distribution changes that remain detectable even in low-light conditions. This parameter transformation resolves the contradiction by making detection independent of illumination intensity.
3Measurement precision
If chromaticity analysis is performed using gain-adjusted thresholds, then accurate occlusion detection is achieved in varying lighting conditions, but the complexity of the detection algorithm increases
Solution Approach 1:
The patent introduces gain-adjusted thresholds that scale with the camera's gain setting. Instead of using fixed threshold values, the system dynamically adjusts the occlusion detection thresholds based on the gain parameter, creating a linear relationship between gain and threshold. This approach maintains detection accuracy across varying lighting conditions and gain settings while avoiding the need for complex adaptive algorithms, thus resolving the contradiction between precision and complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and timely detection of camera occlusion, improving camera functionality and enabling autonomous vehicles to make informed decisions by distinguishing between occlusion and dark scenes, even in low-light conditions.
Implementation Method 1
capturing an image using the camera, the camera having red, green, and blue pixels each including a photosensor; determining, by one or more processors, output values for the photosensors of each of the red pixels, green pixels, and blue pixels
Data Source
AI summary
The technology relates to determining whether a camera is occluded. For instance, an image may be captured using a camera having red, green, and blue pixels each including a photosensor. Output values for the photosensors of each of the red pixels, green pixels, and blue pixels may be determined for the images. The output values of the green pixels may be compared to one or more of the output values of the red pixels or the output values of the blue pixels. That the camera is occluded is determined based on the comparison.