System with a dishwasher, method, and computer program product
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Solution Overview
Problem
Existing dishwashers lack the capability to effectively utilize three-dimensional information for optimizing wash programs, leading to potential blockages and suboptimal cleaning results due to the inability to accurately assess dish arrangement and configuration within the dishwasher.
Innovation Solution
A dishwasher system equipped with a single image capturing device that captures a 2D image and metadata, processes it to generate a 3D image of the dish rack, and adjusts wash programs based on this 3D information, using neural networks to determine optimal loading, blockages, and adjust wash parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single image capturing device is used to capture 2D images, then device complexity is reduced, but three-dimensional information is lost
Solution Approach 1:
The system uses a neural network to transform 2D image data into a 3D representation (depth map) by adding a depth dimension to the captured images. This allows the system to recover three-dimensional information from two-dimensional captures, resolving the information loss while maintaining device simplicity.
Solution Approach 2:
A neural network processing unit acts as an intermediary between the single image capturing device and the control system. The neural network processes the 2D images and generates depth information, effectively mediating the transition from 2D captures to 3D understanding without requiring multiple physical cameras.
2Measurement precision
If multiple image capturing devices are used to capture 3D images, then three-dimensional information is improved, but device complexity increases
Solution Approach 1:
Instead of using multiple physical cameras to capture 3D information directly, the system creates a virtual 3D copy (depth map) from 2D images through neural network processing. This copying approach achieves 3D measurement precision without the complexity of multiple imaging devices.
Solution Approach 2:
The patent replaces the mechanical/optical system of multiple cameras with a computational system (neural network) that processes 2D images to generate depth information. This substitution eliminates the need for complex multi-device hardware while achieving similar 3D measurement capabilities.
3Productivity
If 3D image processing is implemented, then wash program optimization is improved, but computational requirements increase
Solution Approach 1:
The neural network for 3D reconstruction is trained in advance offline, so that during actual dishwasher operation, the pre-trained model can quickly process images without requiring extensive real-time computation. This preliminary training separates the heavy computational burden from the operational phase.
Solution Approach 2:
The system uses the dishwasher's existing processing resources (microcontroller, memory) to perform the neural network inference, rather than requiring an external powerful computer. The dishwasher essentially serves itself by utilizing its own computational capabilities for 3D processing.
Data Source
AI summary
A system includes a dishwasher having a dish rack to receive dishes. An image capturing device captures a 2D image of the dish rack of the dishwasher and the dishes, and an image processing device determines a 3D image based on the captured 2D image and additionally captured metadata of the captured 2D image. A control device executes a wash program as a function of the determined 3D image.