Lens And Protective Dome Cleaning Using AI/ML Defect Detection
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Solution Overview
Problem
Accumulation of dust, snow, and debris on the lens and protective dome of flame detectors compromises their performance, leading to false alarms or failure in fire detection, and manual cleaning is challenging and risky.
Innovation Solution
An AI/ML-based system analyzes images to detect defects on the lens and dome, actuates a wiper for cleaning, and generates alerts for continued defects, ensuring optimal image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual cleaning is performed to remove dust and debris from the lens, then image quality is improved, but operational costs increase and safety risks arise
Solution Approach 1:
The system automatically detects lens defects using AI/ML image analysis and activates a wiper mechanism to clean the lens without human intervention. The processor continuously monitors image quality by analyzing MTF values and triggers cleaning only when defects are detected, making the system self-maintaining and eliminating the need for manual cleaning operations.
Solution Approach 2:
The patent replaces manual mechanical cleaning with an automated electronic system that uses AI/ML algorithms to detect lens defects through image analysis. When defects are detected, an electric wiper mechanism is activated to clean the lens, substituting human-operated mechanical cleaning with an automated electromechanical system that reduces operational costs and safety risks.
2Ease of operation
If automated cleaning is implemented to reduce manual intervention, then operational costs decrease, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single unified platform: the image capturing device serves both its primary surveillance function and the secondary function of lens defect detection. The processor performs both image analysis for defect detection and control of the wiper mechanism, eliminating the need for separate dedicated cleaning hardware and reducing overall system complexity despite the automated capability.
Solution Approach 2:
The system uses its existing image capturing capability to automatically monitor its own lens condition, making the device self-diagnostic. By leveraging the already-present camera and processor for dual purposes (surveillance and self-monitoring), the system achieves automated maintenance without adding significant complexity, as the same components serve multiple functions.
3Measurement precision
If continuous image analysis is performed to detect lens defects, then cleaning accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous analysis, the system performs image analysis periodically or event-driven. The processor analyzes images to calculate MTF values and detect lens defects only at scheduled intervals or when specific conditions are met, rather than continuously. This periodic operation maintains high defect detection accuracy while significantly reducing energy consumption compared to continuous real-time analysis.
Solution Approach 2:
The system applies partial action by analyzing only specific image regions or parameters relevant to lens defect detection rather than processing entire images continuously. By focusing computational resources on calculating MTF values from selected image data and comparing against thresholds, the system achieves sufficient detection accuracy with reduced energy expenditure compared to exhaustive continuous analysis of all image data.
Data Source
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AI summary
A method and system are disclosed. The system comprises an image capturing device and at least one processor communicatively coupled to the image capturing device. The image capturing device is configured to capture one or more images. The at least one processor is configured to receive one or more images from the image capturing device. Further, the at least one processor is configured to analyze the received one or more images using AI/ML model. Further, the at least one processor is configured to determine a MTF value from each of the one or more images. Further, compare the determined MTF value with a predefined MTF value, and identify one or more defects on a lens and/or a protective dome of the image capturing device by analyzing the one or more images using the AI/ML model or by determining the determined MTF value is lower or higher by a predefined limit.