Vision-Based Cookware Detection Using Image Classification
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Solution Overview
Problem
Existing cookware detection systems lack accuracy and are often prone to false positives, and specialized cookware with built-in sensors is costly, making it undesirable for average users.
Innovation Solution
A system using a vision sensor and classifier to detect cookware by capturing imagery of a cooktop, training a classifier with positive and negative image datasets, and allowing user input for correction to refine the classification, enabling periodic re-training for improved accuracy without additional costly hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion sensors or pressure sensors are used to detect cookware, then the system can detect objects on the cooktop, but the detection accuracy is poor and false positives occur
Solution Approach 1:
The patent replaces mechanical sensors (motion sensors, pressure sensors) with a vision-based system using a camera and image processing algorithms. This substitution allows for more precise visual identification of cookware characteristics such as shape, size, and material properties, thereby improving detection accuracy and reducing false positives from non-cookware objects.
Solution Approach 2:
The system creates a visual model or representation of cookware by capturing images and analyzing visual features. By comparing the captured image against known cookware patterns and characteristics, the system can accurately identify cookware without physical contact, improving both precision and reliability of detection.
2Measurement precision
If specialized cookware with built-in sensors is used, then cookware detection accuracy is improved, but the cost increases significantly
Solution Approach 1:
The patent develops a universal vision-based detection system that can identify various types of cookware without requiring each piece to have specialized embedded sensors. The system uses general-purpose image processing techniques to detect diverse cookware items, making the solution applicable to existing cookware sets and eliminating the need for expensive specialized equipment.
Solution Approach 2:
Instead of requiring physical sensors embedded in each cookware item, the system creates a digital visual model of cookware characteristics. By capturing and analyzing visual features from standard cameras, the system replicates the functionality of expensive sensor-equipped cookware using affordable imaging technology.
3Measurement precision
If a classifier is trained with user feedback, then detection accuracy is improved over time, but the system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where user corrections to classification errors are captured and used to retrain the classification algorithm. This continuous learning process improves detection accuracy over time by adjusting the model based on actual user observations, allowing the system to adapt to specific kitchen environments and cookware sets.
Solution Approach 2:
The system performs self-improvement by automatically retraining its classification model using user feedback. Rather than requiring manual reconfiguration or expert intervention, the system autonomously learns from correction data and updates its own performance, reducing the need for complex external management while improving accuracy.
Data Source
AI summary
Systems and methods for cookware detection are provided. One example system includes a vision sensor positioned so as to collect a plurality of images of a cooktop. The system includes a classifier module implemented by one or more processors. The classifier module is configured to calculate a cookware score for each of the plurality of images and to use the cookware score for each of the plurality of images to classify such image as either depicting cookware or not depicting cookware. The system includes a classifier training module implemented by the one or more processors. The classifier training module is configured to train the classifier module based at least in part on a positive image training dataset and a negative image training dataset.