Camera Cover Dirt Detection Using Multi-Exposure Grayscale Ratios
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Solution Overview
Problem
Existing ToF camera systems suffer from low precision in determining the dirty level of their camera covers due to dust accumulation, which affects ranging accuracy.
Innovation Solution
A method involving obtaining multiple grayscale images with different exposure times and comparing pixel ratios to determine the dirty state of the camera cover, using a reference cover for background calibration and setting thresholds for dirt levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple comparison method is used to determine lens dirtiness, then the detection process is simple and fast, but the precision of dirt level determination is low
Solution Approach 1:
The patent divides the detection process into multiple segments: obtaining first and second images under different conditions, extracting grayscale values from corresponding pixels, calculating ratios, and comparing with thresholds. This segmentation allows each step to be optimized independently, improving overall precision while maintaining operational clarity
Solution Approach 2:
The patent performs preliminary actions by obtaining a first image with a clean reference cover before detecting the second image with the to-be-detected cover. This preliminary reference image is used to establish baseline grayscale values and ratios, enabling more accurate dirt detection in the subsequent measurement phase
2Measurement precision
If multiple image samples and ratio calculations are used to improve detection accuracy, then the precision of dirt detection is improved, but the complexity of the detection method increases
Solution Approach 1:
The patent creates a copy of the detection process by obtaining multiple images (first image with reference cover, second image with to-be-detected cover) under similar but distinct conditions. By comparing these copies through grayscale ratio calculations, the system achieves higher precision without requiring complex additional hardware
Solution Approach 2:
The patent introduces an intermediary element - the grayscale ratio calculation - that mediates between the raw image data and the final dirt detection result. This intermediary step transforms complex image comparison into a simplified ratio metric that can be easily thresholded, reducing overall system complexity while maintaining high precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy and reliability of dirt detection on camera covers by using multiple image samples and thresholds, reducing incorrect determinations and enhancing the precision of dirty state assessment.
Implementation Method 1
an imaging sensor disposed at an inner side of a camera cover and configured to receive the laser pulse, form a first normal-frame image, a first detection-frame image, a second normal-frame image, and a second detection-frame image
Data Source
AI summary
Embodiments of the present invention provide a dual-modality bionic vision sensor. A first-type current-mode active pixel sensor (APS) circuit can simulate excitatory rod cells to perceive light intensity gradient information in a target light signal, thereby improving a dynamic arrange of an image sensed by a bionic vision sensor and its shooting speed. In addition, a first-type control switch is introduced for each of non-target first-type photosensitive devices to control the obtained light intensity gradient information, and to adjust the dynamic arrange of the image sensed by the bionic vision sensor, thereby adjusting the shooting speed and realizing a reconfigurable effect. A voltage-mode APS can simulate cone cells to output a target voltage signal representing light intensity information in the target light signal, and to perceive the light intensity information in the target light signal. In this way, the obtained light intensity information represented by the target voltage signal has a higher precision, thereby ensuring the image quality.