Vehicle Camera Lens Contamination Detection by Pixel Brightness Change
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Solution Overview
Problem
Camera lenses in vehicles can become contaminated by external factors such as rain, snow, dust, and dust, leading to failures in object detection and measurement due to distorted images, which can cause malfunctions in advanced driver assistance systems and video recording.
Innovation Solution
A vehicle system that includes a camera, memory, and a controller to detect foreign materials on the lens by analyzing pixel brightness changes, outputting a warning message, and activating a sensor cleaning system when necessary, using histogram equalization and segment division to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the camera lens is exposed to external environment for continuous operation, then the camera can capture images around the vehicle, but the lens surface becomes contaminated with external contaminants such as rain, snow, dust
Solution Approach 1:
The system performs preliminary detection of lens contamination by analyzing brightness change rates of pixels before contamination significantly degrades image quality. The controller continuously monitors pixel brightness and detects contamination early when brightness change rate becomes abnormally low, allowing for timely cleaning activation before the contamination causes detection failures or measurement errors.
2Reliability
If the camera lens is cleaned frequently to maintain image quality, then the reliability of image capture is improved, but the complexity of the cleaning system increases
Solution Approach 1:
The system uses a feedback mechanism where the controller continuously analyzes pixel brightness data from the camera and automatically activates the cleaning system only when contamination is detected. The brightness change rate serves as a feedback signal that triggers cleaning operations conditionally, rather than operating continuously or on fixed schedules, thereby maintaining reliability while minimizing unnecessary cleaning cycles.
Solution Approach 2:
The camera system performs self-diagnosis by analyzing its own captured image data to detect lens contamination. The controller uses the camera's pixel brightness information to determine when cleaning is needed, eliminating the need for separate sensors or complex detection systems. The system serves itself by using its primary function (image capture) to monitor its own health status.
3Measurement precision
If the brightness detection threshold is set to be highly sensitive to detect early contamination, then the detection precision is improved, but false detection increases due to normal brightness variations
Solution Approach 1:
The system changes the detection parameter from absolute brightness values to brightness change rate. Instead of comparing pixel brightness to a fixed threshold that would cause false detections during normal lighting variations, the system monitors how much the brightness changes over time. Contamination causes abnormally low brightness change rates because contaminants block light uniformly, whereas normal scene changes cause larger brightness variations. This parameter transformation enables sensitive contamination detection while filtering out false positives from environmental lighting changes.
Data Source
AI summary
A vehicle includes at least one camera configured to photograph an image around the vehicle, a memory configured to store image data input from the at least one camera, and a controller. The controller is configured to determine that a foreign material is present on the at least one camera and to output a warning message to a user when a brightness change rate of at least one pixel among each pixel of an image frame included in the image data is less than or equal to a reference value and is lower than an average brightness of other pixels except for the at least one pixel.