Lens Contamination Detection via Pixel Trajectory Analysis
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Solution Overview
Problem
Existing camera-based assistance systems in passenger vehicles face challenges in detecting and addressing lens contamination, which affects image quality and can lead to inaccurate scene analysis, potentially causing safety issues due to the ambiguity between blurred regions caused by lack of texture and lens soiling.
Innovation Solution
A method that assigns transition values to pixels in image frames using a transition indicator, computes expectation and satisfied transition values over time, and derives a cleanliness value to determine lens contamination, utilizing three-dimensional coordinates and vehicle movement data to track pixel trajectories and distinguish between clean and contaminated areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lens contamination detection is performed using traditional methods (edge intensity threshold, brightness comparison), then lens contamination can be detected, but false alarms occur due to ambiguity between blurred regions from lack of texture and lens soiling
Solution Approach 1:
The patent introduces temporal dimension by analyzing pixel trajectories across multiple image frames. Instead of making detection decisions based on single-frame properties, the system tracks whether pixels maintain their expected trajectories over time, adding a temporal dimension to distinguish true lens contamination from scene-induced blurring.
Solution Approach 2:
The system pre-calculates and stores expected pixel trajectories based on vehicle motion data before contamination detection is needed. These trajectory expectations serve as reference data that enables rapid and accurate contamination detection when actual image data is analyzed, eliminating the need for complex real-time calculations during detection.
2Object-affected harmful factors
If camera positioning is optimized to reduce contamination risk, then contamination frequency decreases, but image quality and detection capability may be compromised
Solution Approach 1:
The system employs self-diagnostic capabilities by automatically detecting lens contamination through trajectory analysis of image data. The camera system monitors its own optical health without requiring external intervention, enabling timely detection and response to contamination while maintaining optimal positioning for image quality.
3Extent of automation
If automatic lens contamination detection is implemented, then appropriate cleaning actions can be triggered, but system complexity increases
Solution Approach 1:
The patent uses vehicle motion data as an intermediary to establish expected pixel trajectories. This intermediary information simplifies the detection process by providing a reference framework against which actual pixel positions can be compared, reducing the computational complexity of contamination detection while maintaining high automation levels.
Data Source
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AI summary
A method for detecting contaminations on a lens of a vehicle camera. Transition values are assigned to pixels of image frames. Three-dimensional coordinates are assigned to the pixels by using a simplified model of the scene. Using the assigned coordinates and movement data, trajectories are assigned to the pixels. An expectation value is derived from a transition value of a starting pixel of a trajectory and a satisfied transition value is derived from transition values along the trajectory. The expectation value and the satisfied transition value are accumulated over time, and a cleanliness value is derived from a ratio of the accumulated satisfied transition value to the accumulated expectation value.