Stereo Camera Sensor Verification via Image Comparison
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Solution Overview
Problem
Current methods for checking the functionality of stereo camera image sensors are inadequate, as they fail to reliably detect errors such as stuck-at pixels, address decoder errors, and contamination, especially in static scenarios and single-channel architectures, which can lead to safety failures in security applications.
Innovation Solution
A method that compares the recorded images from two image sensors with a perspective offset using a stereo algorithm to estimate disparities, allowing for the identification of matching image sections and detecting errors by analyzing differences in pixel values and image features, which is integrated into the depth map calculation for real-time functionality checking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current noise level comparison method is used to check image sensor functionality, then the test can be performed, but the detection reliability is insufficient because defective pixels can show noise corresponding to expectations
Solution Approach 1:
The patent uses the stereo camera's dual-camera architecture to create a copy of the scene from a slightly different perspective. By comparing corresponding pixels between the two images, the system can detect defective pixels that would otherwise be indistinguishable from normal noise in a single-image test.
Solution Approach 2:
The system implements continuous monitoring of pixel correspondence between stereo images, providing real-time feedback on image sensor health. When discrepancies exceed thresholds, the system can trigger alerts or corrective actions, enabling proactive maintenance.
2Reliability
If FPN mixed-up detection method is used to detect address decoder errors, then address errors can be detected, but the FPN cannot be evaluated with sufficient reliability because it is overlaid by the actual image
Solution Approach 1:
The stereo camera captures a duplicate view of the scene, allowing the system to separate fixed pattern noise from actual scene content by comparing the two images. Address decoder errors manifest as consistent mismatches in corresponding pixel locations across the stereo pair.
Solution Approach 2:
The system performs preliminary comparison of stereo image pairs to establish baseline FPN characteristics before overlaying with actual image content, enabling reliable separation and evaluation of FPN components.
3Reliability
If verification circuits are integrated in the image sensor to verify functionality, then functional verification can be performed, but the image sensor becomes more complex to produce and requires additional space
Solution Approach 1:
The stereo camera's dual-camera system serves multiple functions: it captures depth information for 3D imaging while simultaneously providing redundant data for image sensor verification. This eliminates the need for dedicated verification circuits, as the second camera acts as both a functional component and a verification tool.
Solution Approach 2:
The stereo camera system performs self-verification by using its own dual-camera architecture to monitor and detect image sensor defects. The system verifies its own functionality without requiring external verification equipment or additional on-sensor circuitry.
4Reliability
If protective fields are configured to prevent operator intrusion, then safety protection is provided, but the system may trigger false safety stops due to undetected image sensor errors
Solution Approach 1:
The continuous comparison of stereo image pairs provides real-time feedback on sensor health, enabling the safety system to distinguish between genuine safety threats and artifacts caused by defective pixels or sensor errors, thereby reducing false positives.
Data Source
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AI summary
A safe stereo camera (10) is described, comprising at least one first image sensor (16a) for capturing a first raw image (36a) and a second image sensor (16b) for capturing a second raw image (36b) of a monitored area (12) from a displaced perspective, a stereoscopic unit (26) for generating a depth map from the two raw images (36a-b), and a test unit (28) for verifying the functionality of the image sensors (16a-b). The test unit (28) is configured to detect errors in the image sensors (16a-b) by comparing first image information from a first image window (40a) of the first raw image (36a) with second image information from a second image window (40b) of the second raw image (36b) and by selecting the image windows (40a-b) based on information from the stereoscopic unit (26) when generating the depth map.