Low-Light Camera Occlusion Detection Using RGB Gain Thresholds
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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 that analyzes the output values of 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, allowing for real-time detection and potential remedial actions like cleaning the camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera output values are analyzed to detect occlusion, then occlusion detection capability is improved, but false detection of dark scenes as occlusions increases
Solution Approach 1:
The patent analyzes chromaticity ratios (r, g, b) derived from red, green, and blue pixel output values to detect occlusions. By examining the color composition and ratios of different wavelength ranges, the system can distinguish between actual occlusions and dark scenes, as occluded cameras typically show abnormal chromaticity patterns compared to cameras capturing dark but visible scenes.
Solution Approach 2:
The system dynamically adjusts detection parameters including gain-based threshold values and chromaticity ratio thresholds based on lighting conditions. By changing these parameters according to the captured image characteristics, the system maintains high detection accuracy across varying ambient light levels while minimizing false positives from dark scenes.
2Measurement precision
If chromaticity analysis is performed to distinguish occlusion from dark scenes, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the image into multiple wavelength ranges (red, green, blue channels) and calculates chromaticity ratios for each segment independently. This segmentation allows the system to process different color components separately using simple ratio calculations, avoiding the need for complex full-spectrum analysis while maintaining accurate occlusion detection capability.
3Reliability
If real-time occlusion detection is implemented, then autonomous vehicle safety is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential chromaticity ratio parameters (r, g, b) from the captured image and uses these extracted values for occlusion detection, rather than processing the entire image data. This extraction approach focuses computational resources on the critical color composition analysis, enabling real-time detection without unnecessary processing overhead.
Solution Approach 2:
The system performs chromaticity analysis on a representative subset of pixels or uses downsampled image data for occlusion detection rather than analyzing every pixel in full resolution. This partial action approach provides sufficient detection accuracy for safety-critical occlusion identification while significantly reducing processing time compared to exhaustive full-image 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 accurate and timely detection of camera occlusion, improving camera functionality and autonomous vehicle operations by preventing errors due to dark scenes being misinterpreted as occlusions, especially 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.