Dirty Monocular Camera Detection via Image Metric Comparison
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Solution Overview
Problem
Autonomous driving vehicles face challenges in accurately detecting obstacles due to dirty camera sensors, which can be affected by environmental contaminants like dust, dirt, and varying light conditions, leading to reduced image quality and reliability.
Innovation Solution
A method for identifying dirty cameras in a monocular camera system with multiple cameras of different focal lengths by capturing images and determining image metrics such as brightness or contrast, where a camera is deemed dirty if its metrics differ by more than 10% from others, triggering a cleaning action if the condition persists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a monocular camera system with multiple cameras of different focal lengths is used, then the field of view and detection capability are improved, but the ability to directly compare images to detect dirt is lost
Solution Approach 1:
The patent transforms the image comparison problem from direct pixel-to-pixel comparison to comparison based on extracted parameters (brightness, contrast, sharpness metrics). By changing the comparison parameter from raw image data to derived quality metrics, cameras with different focal lengths can be compared meaningfully to detect dirt conditions
Solution Approach 2:
The patent introduces image quality metrics (brightness, contrast, sharpness) as an intermediary between the raw images from different cameras and the dirt detection decision. These metrics serve as a common language to compare cameras with different focal lengths, enabling indirect comparison through a mediating measurement layer
2Measurement precision
If image metrics are compared to detect dirty cameras, then detection accuracy is improved, but false positives may occur due to varying light conditions
Solution Approach 1:
The system continuously monitors image metrics over time and uses feedback loops to distinguish between temporary variations (light conditions) and persistent problems (dirt). By comparing current metrics against historical data and thresholds, the system can confirm whether a detected anomaly represents actual dirt or merely environmental variation
Solution Approach 2:
The patent implements dynamic thresholding and adaptive monitoring where the detection criteria adjust based on operating conditions. The system modifies its sensitivity and comparison parameters dynamically to account for varying light conditions, maintaining high detection accuracy while reducing false positives through context-aware decision making
Data Source
AI summary
Systems and methods are disclosed for identifying a dirty camera in a monocular camera comprising n cameras. For each of one or more cycles, a dirty counter variable for each of the n cameras is set to 0. For each of the n cameras, an image is captured from the camera and an image metric is determined for the image, e.g. brightness and/or contrast. If the image metric is 10% greater, or 10% less, than the image metric for any of the other n−1 cameras in the monocular camera, then that camera is determined to be dirty and a corrective action is taken, such as sending an alarm to an occupant the vehicle or initiating a cleaning operation. If the dirty condition persists for a camera that has been cleaned within a threshold period of time (i.e., recently), then an alarm is sent to an operator of the vehicle.