Low-Light Camera Occlusion Detection Using RGB Chromaticity
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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 to compare these values against threshold settings based on image gain, to differentiate between occlusion and capturing a dark scene, with the capability to trigger remedial actions such as cleaning the camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If camera captures image in low ambient lighting conditions, then image capture capability is maintained, but occlusion detection accuracy deteriorates
Solution Approach 1:
The patent utilizes chromaticity analysis by comparing the relative output values of red, green, and blue pixels to detect occlusions. In low-light conditions, the chromaticity signature changes when the lens is occluded versus when capturing a dark scene, allowing differentiation through color ratio analysis rather than absolute intensity measurements.
Solution Approach 2:
The system changes the detection parameter from absolute pixel intensity to chromaticity ratios (comparative output values between color channels). This parameter transformation enables reliable occlusion detection in low-light conditions where absolute intensity values are insufficient, by focusing on the relative relationships between color channels that remain distinguishable even when overall lighting is poor.
2Illumination intensity
If camera configuration parameters (gain, exposure time) are adjusted to capture low-light images, then image quality in dark conditions is improved, but occlusion detection reliability deteriorates
Solution Approach 1:
The patent transforms the detection approach by changing from absolute intensity-based detection to chromaticity ratio-based detection. By computing comparative output values between color channels (e.g., green-to-red ratio, blue-to-green ratio), the system achieves detection reliability that is independent of camera configuration parameters such as gain and exposure time, since these parameters affect all channels uniformly and cancel out in the ratio computation.
Solution Approach 2:
The patent exploits the asymmetric response of different color channels to occlusion versus dark scenes. When the lens is occluded, the chromaticity signature exhibits a distinct asymmetric pattern compared to when capturing a dark scene, even under identical camera configurations. This asymmetry in chromaticity behavior across color channels provides a reliable indicator for occlusion detection that remains consistent regardless of gain or exposure time settings.
3Measurement precision
If chromaticity analysis is performed to differentiate occlusion from dark scenes, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image data processing into distinct color channel computations (red, green, blue) followed by ratio calculations. This segmentation allows the system to process each color channel independently and then combine results through simple comparative operations, reducing overall computational complexity while maintaining high detection accuracy through chromaticity analysis.
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 real-time detection of camera occlusion independently of camera configuration, improving operational reliability and enabling autonomous vehicles to make informed driving decisions by distinguishing between occlusion and dark scenes.
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
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.