Method and system for sensor maintenance
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Solution Overview
Problem
Automated appliances, such as smart ovens, face challenges in foodstuff recognition due to camera soiling or obstruction, leading to reduced accuracy and labelling discrepancies, especially when accessories or appliance surfaces are dirty, making it difficult to determine camera cleanliness 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 the camera is located within the cooking cavity to enable automated foodstuff recognition, then the appliance can automatically identify food to be cooked, but the camera becomes soiled or obscured reducing recognition efficacy and accuracy
Solution Approach 1:
The system performs preliminary classification of cavity images to detect camera soiling conditions before foodstuff recognition is attempted. By pre-identifying when the camera lens is obscured, the system can trigger cleaning notifications or adjust operation modes, preventing degraded recognition accuracy from dirty cameras
Solution Approach 2:
The system continuously monitors cavity images and uses classification results to provide feedback about camera cleanliness status. This feedback loop enables the system to detect when the camera becomes soiled during operation and respond appropriately, maintaining recognition efficacy despite the camera's in-situ location
2Measurement precision
If the camera lens is clean but the appliance surfaces or accessories are dirty, then the camera can capture clear images, but labelling discrepancies occur due to dirty backgrounds
Solution Approach 1:
The system extracts and separately classifies the camera cleanliness status from the overall cavity image analysis. By isolating the lens cleanliness assessment from foodstuff identification, the system can detect when images are captured with a clean lens but dirty surrounding surfaces, preventing labelling errors while maintaining clear image capture
Solution Approach 2:
The system performs preliminary classification to determine camera and cavity cleanliness status before attempting foodstuff labelling. This preliminary assessment identifies conditions where clear images may still produce incorrect labels due to dirty accessories or surfaces, allowing the system to adjust its labelling process or notify users
3Measurement precision
If the appliance is empty to enable reliable lens cleanliness determination, then labelling accuracy improves, but the appliance cannot perform cooking operations
Solution Approach 1:
The system segments the classification task into separate functions: one classifier determines camera and cavity cleanliness status, while another performs foodstuff recognition. This segmentation allows the system to accurately assess lens cleanliness during cooking operations without requiring the appliance to be empty, maintaining both detection accuracy and operational productivity
4Reliability
If cavity measurements are sampled continuously to detect camera soiling in real-time, then maintenance can be timely, but computational intensity and memory usage increase
Solution Approach 1:
The system implements periodic sampling of cavity measurements at optimized intervals rather than continuous monitoring. This periodic approach maintains reliable detection of camera soiling conditions and timely maintenance notifications while significantly reducing computational intensity and memory usage compared to continuous sampling
Solution Approach 2:
The system dynamically adjusts the sampling rate and classification frequency based on operational context and detected soiling trends. By changing parameters such as sampling interval and analysis depth, the system maintains effective maintenance timing while optimizing computational resource utilization
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.