Automated Camera Cleanliness Detection for Appliance Vision Systems
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Solution Overview
Problem
Automated appliances with in-situ cameras face reduced food recognition accuracy due to camera soiling or obstruction, and existing computer-vision techniques struggle with labeling discrepancies caused by dirty lenses or accessories, making it difficult to determine camera cleanliness, especially when food is present.
Innovation Solution
A method and system for dirty camera detection that involves detecting state changes, sampling cavity measurements, determining class labels for camera cleanliness, and facilitating appliance use based on these labels, including notifications and potential cleaning actions, which improves accuracy by utilizing images of empty cavities and employing a multi-task classification system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computer-vision-based techniques are used for foodstuff recognition, then automated appliance functionality is improved, but camera lens soiling and obstruction reduce recognition accuracy
Solution Approach 1:
The system performs preliminary classification of cavity images to detect camera lens soiling conditions before foodstuff recognition is attempted. By identifying dirty lens conditions in advance through image analysis, the system can flag potential accuracy issues and adjust operations accordingly, preventing erroneous recognition results.
Solution Approach 2:
The system continuously monitors cavity images and provides feedback on camera lens cleanliness status. This feedback mechanism allows the appliance to recognize when the lens is soiled and adjust its foodstuff recognition operations, potentially triggering lens cleaning cycles or alerting the user to clean the lens, thereby maintaining recognition accuracy despite the soiling condition.
2Productivity
If images are captured in a dirty appliance cavity, then computer-vision processing can proceed, but labeling discrepancies occur due to dirty surfaces and accessories
Solution Approach 1:
The classification system segments the image analysis task by first identifying and characterizing dirty surfaces and accessories separately from foodstuff. By dividing the classification process into distinct stages - detecting cavity conditions, identifying dirty elements, and then performing foodstuff recognition - the system can account for environmental factors and improve labeling accuracy while maintaining processing throughput.
Solution Approach 2:
The system introduces an intermediary classification layer that analyzes cavity conditions (dirty surfaces, accessories, lens status) before performing foodstuff labeling. This intermediary step acts as a mediator that separates the effects of dirty environmental factors from actual foodstuff identification, allowing the system to proceed with processing while adjusting for potential labeling discrepancies.
3Measurement precision
If camera lens cleaning is performed frequently, then recognition accuracy is maintained, but appliance operation time is reduced due to cleaning interruptions
Solution Approach 1:
The system dynamically adjusts camera lens cleaning frequency based on detected soiling conditions rather than following a fixed schedule. By continuously monitoring cavity images for signs of lens contamination and adapting cleaning operations accordingly, the system maintains recognition accuracy only when necessary, maximizing appliance operational time while preventing accuracy degradation.
Solution Approach 2:
The system changes the operational parameters of camera lens cleaning based on detected conditions. Instead of fixed-interval cleaning, the system adjusts cleaning frequency and timing based on measured lens cleanliness status from image analysis, transitioning from a static maintenance schedule to a condition-based dynamic maintenance strategy that optimizes both accuracy and operational time.
4Measurement precision
If manual inspection of camera lens cleanliness is performed, then accurate detection is possible, but user time and convenience are reduced
Solution Approach 1:
The system performs self-inspection of camera lens cleanliness by automatically analyzing cavity images to detect lens soiling conditions. The appliance monitors its own sensor status without requiring user intervention, identifying dirty lens conditions through image processing and classification algorithms, thereby maintaining high detection accuracy while preserving user convenience.
Solution Approach 2:
The system replaces manual mechanical inspection of the camera lens with an automated optical-electronic inspection system. Instead of requiring users to physically examine the lens, the system uses the camera itself to capture images and automated classification algorithms to detect lens cleanliness status, substituting a manual mechanical process with an automated sensor-based system that maintains accuracy while improving ease of operation.
Data Source
AI summary
The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.