Stereo Depth Image Processing for Lens Contamination Detection
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Solution Overview
Problem
Existing image processing systems for depth cameras struggle to quickly detect lens contamination and extreme contrasts, leading to incomplete depth information and potential safety hazards in robotics and autonomous driving systems.
Innovation Solution
An image processing system that uses a detection mechanism for stereo-based depth cameras, capable of detecting lens contamination and extreme contrasts by correlating data between two light sensors within a single image, and estimating missing depth information without exposure adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional 2D LiDAR sensors are added to supplement depth cameras for safety certification, then safety reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary detection mechanism that analyzes existing camera images to identify circumstances affecting depth perception (lens contamination, extreme contrasts). This intermediary layer processes image data to generate detection results without requiring additional sensors, thereby maintaining safety reliability while avoiding increased device complexity
Solution Approach 2:
The patent creates a virtual representation of depth information by analyzing 2D camera images and inferring depth-related circumstances. Instead of physically adding LiDAR sensors, the system copies the safety detection function by processing existing visual data through algorithms that identify contamination and contrast issues affecting depth perception
2Speed
If traditional depth camera systems are used without rapid detection mechanisms, then device complexity is reduced, but response time to safety hazards increases
Solution Approach 1:
The patent performs preliminary detection of circumstances affecting depth perception by analyzing current camera images for lens contamination and extreme contrasts. By proactively identifying these issues before they cause safety hazards, the system achieves rapid detection without requiring complex real-time response mechanisms or additional hardware
Solution Approach 2:
The patent focuses processing resources on specific critical areas - detecting only lens contamination and extreme contrast conditions that affect depth perception. This partial action approach concentrates computational effort on safety-critical detections rather than comprehensive scene analysis, achieving fast detection speeds with moderate processing complexity
3Measurement precision
If lens contamination or extreme contrasts are not quickly detected, then processing simplicity is maintained, but depth information accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the detection of lens contamination and extreme contrasts provides information about the quality of depth perception. This feedback allows the system to identify when depth information may be inaccurate due to environmental factors, enabling corrective actions or warnings without requiring complex real-time compensation mechanisms
Solution Approach 2:
The patent replaces physical depth sensing mechanisms with image processing-based detection. Instead of using additional mechanical sensors to verify depth accuracy, the system substitutes computational analysis of camera images to detect circumstances that would affect depth measurement precision, thereby maintaining measurement accuracy through software rather than hardware
Data Source
AI summary
According to one aspect of the present disclosure, an image processing system is provided, comprising a memory and a processor, wherein the processor is configured to determine a depth image from at least two camera images of a scene, to determine at least one area in the depth image where depth information is insufficient, for the at least one determined area in the depth image where depth information is insufficient, to determine, for each of the at least two camera images, a camera image area of the camera image that corresponds to the determined area in the depth image where depth information is insufficient, to perform a matching of at least parts of the determined camera image areas, to determine a cause for the insufficient depth information from a result of the matching, and to further process the depth image depending on the determined cause.