Image quality detection for a camera assembly in a laundry appliance
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Solution Overview
Problem
Camera assemblies in laundry appliances face issues with image quality degradation due to contamination from water, detergent, and steam, which affects the reliability of data obtained for wash performance monitoring.
Innovation Solution
A laundry appliance system that includes a camera assembly and a controller using an autoencoder neural network to analyze image quality, determining when the quality drops below a threshold and triggering a responsive action, such as a lens cleaning process, to maintain image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a camera assembly is positioned within the wash chamber to monitor load conditions, then useful information related to wash performance can be obtained, but the camera lens will be exposed to water, detergent, and steam causing contamination and image quality degradation
Solution Approach 1:
The patent extracts the camera assembly from the wash chamber environment by positioning it in the cabinet exterior, removing it from direct exposure to water, detergent, and steam. This allows the camera to maintain image quality while still monitoring the wash chamber through strategic positioning and timing of image capture during specific operational phases.
2Productivity
If the camera operates continuously to monitor wash performance, then real-time data can be obtained, but image quality degrades due to cumulative exposure to contaminants
Solution Approach 1:
The camera operates periodically rather than continuously, capturing images at specific intervals during the wash cycle when the basket is stationary and the chamber is accessible. This periodic operation reduces cumulative exposure to contaminants while maintaining sufficient monitoring capability to detect load type, water level, and out-of-balance conditions.
Solution Approach 2:
The system captures images at predetermined phases of the wash cycle (such as during fill, drain, or spin phases when the basket is stationary), preparing data for analysis before the next operational phase begins. This approach ensures quality images are obtained at critical monitoring points without requiring continuous operation.
3Loss of information
If the camera lens is exposed to the wash chamber environment for monitoring purposes, then wash performance data can be collected, but the lens accumulates dirt, soap scum, and grime affecting image quality
Solution Approach 1:
The camera assembly is extracted from the wash chamber interior and positioned in the cabinet exterior, eliminating direct contact with water, detergent, and steam that cause lens contamination. This positioning allows the camera to capture images of the wash chamber through openings or during specific phases without the lens being exposed to harmful contaminants.
Solution Approach 2:
The system uses the cabinet interior space as an intermediary environment for the camera, positioned between the clean external environment and the harsh wash chamber environment. This intermediate positioning allows image capture while protecting the lens from direct exposure to contaminants through strategic placement and timing.
Data Source
AI summary
A laundry appliance includes a basket that is rotatably mounted within a cabinet and that defines a chamber for receiving a load of clothes. A camera assembly is mounted within the cabinet for taking images of the chamber and a controller obtains an image of the chamber when the basket is empty and not rotating. The image is analyzed using an autoencoder neural network process to determine an image quality and a responsive action is implemented when an image quality drops below a quality threshold.