Camera Obstruction Detection via Image Parameter Analysis
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Solution Overview
Problem
A significant percentage of images captured by cameras are of poor quality due to obstruction by dirt, grease, or foreign objects, leading to hazy or poorly detailed images with exaggerated glare.
Innovation Solution
A method involving a computing device that compares parameters of captured images with control parameters from a substantially unobstructed camera, determining a score and accumulating counts to detect if the camera is at least partially obstructed, and providing notifications or indicators for cleaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If camera captures images continuously, then image quantity increases, but image quality deteriorates due to obstruction by dirt, grease or foreign objects
Solution Approach 1:
The system continuously monitors image parameters (sharpness, contrast, brightness distribution) and compares them against threshold values to detect obstructions. When degradation is detected, the system provides feedback to alert users to clean the lens, creating a closed-loop quality control mechanism that maintains image quality standards.
Solution Approach 2:
The system performs preliminary analysis of captured images to detect obstructions before they significantly degrade image quality. By analyzing parameters such as sharpness and contrast in real-time, the system identifies potential issues early and alerts users proactively, preventing poor quality images from being captured.
2Ease of operation
If camera lens is obstructed by dirt or grease, then image capture function continues, but image quality deteriorates with hazy appearance and exaggerated glare
Solution Approach 1:
The system continuously monitors image parameters (sharpness, contrast, brightness distribution) and compares them against threshold values to detect obstructions. When degradation is detected, the system provides feedback to alert users to clean the lens, creating a closed-loop quality control mechanism that maintains image quality standards.
Solution Approach 2:
The patent replaces mechanical inspection methods with automated digital image analysis. Instead of requiring manual inspection of the lens or physical test targets, the system uses computational analysis of image parameters (sharpness, contrast, brightness distribution) to detect obstructions, substituting mechanical/digital hybrid inspection with purely digital signal processing.
3Device complexity
If no obstruction detection is implemented, then device complexity remains low, but image quality reliability deteriorates
Solution Approach 1:
The system continuously monitors image parameters (sharpness, contrast, brightness distribution) and compares them against threshold values to detect obstructions. When degradation is detected, the system provides feedback to alert users to clean the lens, creating a closed-loop quality control mechanism that maintains image quality standards.
Solution Approach 2:
The system performs self-diagnosis by automatically analyzing its own captured images for quality degradation. The camera module works together with the processing system to autonomously detect obstructions without requiring external inspection equipment or manual intervention, enabling the system to self-monitor and self-report quality issues.
Data Source
AI summary
The present disclosure provides example methods operable by computing device. An example method can include receiving an image from a camera. The method can also include comparing one or more parameters of the image with one or more control parameters, where the one or more control parameters comprise information indicative of an image from a substantially unobstructed camera. Based on the comparison, the method can also include determining a score between the one or more parameters of the image and the one or more control parameters. The method can also include accumulating, by a computing device, a count of a number of times the determined score image exceeds a first threshold. Based on the count exceeding a second threshold, the method can also include determining that the camera is at least partially obstructed.