Laundry Tag Recognition for Composite Fabric Wash Course Control
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Solution Overview
Problem
Conventional laundry treatment devices fail to provide an optimal washing course for composite fiber materials with different characteristics, leading to potential damage or inadequate washing when multiple types of laundry are processed together.
Innovation Solution
A laundry treatment device equipped with a camera to capture images of tags on laundry, a processor to analyze material mixing ratios, and a deep learning algorithm to determine customized laundry courses using a laundry course learning model, ensuring appropriate washing parameters for each type of fabric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional laundry treatment devices provide predetermined laundry courses or reflect only user-set parameters, then the device complexity remains low and ease of operation is maintained, but the laundry damage increases and washing appropriateness deteriorates when composite fiber materials are processed
Solution Approach 1:
The laundry treatment device automatically identifies laundry material composition and determines optimal washing courses without user intervention. The image recognition system captures images of laundry tags, processes material information, and selects appropriate washing parameters autonomously, eliminating the need for users to manually input material details while ensuring reliable and appropriate washing for composite fibers.
Solution Approach 2:
The patent replaces manual user input and conventional mechanical course selection with an automated image recognition and deep learning system. The camera captures tag images, the processor identifies material composition through pattern recognition, and the system determines washing courses algorithmically, substituting mechanical parameter setting with intelligent automated determination.
2Manufacturing precision
If the device automatically recognizes laundry materials and analyzes mixing ratios, then the laundry damage is minimized and washing capacity is optimized, but the device complexity and measurement difficulty increase
Solution Approach 1:
The patent introduces laundry tags as intermediaries that carry material composition information. Instead of directly analyzing complex laundry materials, the system captures images of standardized tags containing material data, which serve as mediators between the physical laundry and the digital recognition system, simplifying the detection and measurement process while maintaining high accuracy.
Solution Approach 2:
The system creates a digital copy of laundry material information through image capture and processing. The camera captures visual information from laundry tags, the processor generates encoding data representing material composition, and this digital copy is used for course determination, enabling accurate material recognition without direct physical analysis of the laundry itself.
3Productivity
If deep learning algorithms are used to determine customized laundry courses, then the productivity and washing effectiveness improve, but the loss of information and processing time may increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and encoding laundry material information before the actual washing cycle begins. The image recognition and material analysis are completed in advance, allowing the deep learning algorithm to work with pre-processed data, thereby reducing real-time processing time and improving overall productivity without sacrificing accuracy.
Data Source
AI summary
A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.