Stereo Camera Lens Obstruction Detection and Visual Output Adjustment
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Solution Overview
Problem
Stereo cameras lack technology to effectively detect and adjust for lens obstructions, leading to compromised visual output as image processing software often merges obstructions with non-obstructed imagery, resulting in undesirable outcomes.
Innovation Solution
The system uses processing circuitry to access imagery from multiple lenses, detect disparities, identify obstructions using machine learning techniques or artificial neural networks, and generate visual outputs that either present clear imagery from unobstructed lenses or augment obstructed imagery to remove obstructions, prompting users to clean the lenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image processing software merges imagery from multiple lenses, then visual output can be generated, but obstructions are incorrectly incorporated into the final image degrading quality
Solution Approach 1:
The system performs preliminary detection of lens obstructions by analyzing disparities between stereo camera imagery before merging the images. This early detection allows the system to identify and exclude obstructed regions from the final composite image, preventing quality degradation while maintaining efficient visual output generation
Solution Approach 2:
The system introduces an intermediary processing step that analyzes disparity data between the two lenses to identify obstruction regions. This intermediary analysis acts as a mediator that separates valid scene information from artifact information caused by obstructions, allowing clean imagery to be merged while excluding contaminated regions
2Manufacturing precision
If the system detects and identifies lens obstructions using disparity analysis, then image quality can be maintained, but additional processing complexity is introduced
Solution Approach 1:
The system uses the stereo camera's own dual-lens configuration to self-diagnose lens obstructions by analyzing disparities between the two views. The existing stereo processing pipeline is leveraged to detect obstructions without requiring external sensing devices, maintaining image quality while avoiding additional hardware complexity
Solution Approach 2:
The system changes the analysis parameter from standard stereo depth estimation to obstruction detection by identifying regions where disparity patterns deviate from expected scene geometry. This parameter reorientation allows the existing processing architecture to serve dual purposes: maintaining depth perception while detecting lens contaminants
3Manufacturing precision
If the system presents clear imagery from unobstructed lenses, then visual output quality improves, but information from the obstructed lens is lost
Solution Approach 1:
The system merges imagery from both lenses selectively, combining clear regions from the obstructed lens with all regions from the unobstructed lens. This selective merging preserves valuable information from the obstructed lens where possible while incorporating complete information from the clear lens, maximizing overall information retention while maintaining visual quality
Data Source
AI summary
A computer accesses first imagery from a first lens and second imagery from a second lens. The first lens and the second lens are components of a stereo camera. The computer detects a disparity between the first imagery and the second imagery. The computer identifies an obstruction on the first lens based on the detected disparity. The computer generates a visual output associated with the stereo camera based on the obstruction. The computer causes display of the generated visual output.