Liquid Impurity Inspection Using Bubble Tracking in Image Sequences
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Solution Overview
Problem
Existing inspection methods struggle to accurately distinguish between air bubbles and impurities in liquid products, leading to false rejects due to the formation of bubbles during container rotation, especially in solutions like saline and sugar, and containers with elliptic bases, which traditional algorithms cannot differentiate with sufficient accuracy.
Innovation Solution
A method using image processing and a convolutional neural network (CNN) to track foreign bodies in liquid products, identifying micro-bubbles and bubbles by analyzing image sequences, and classifying them based on shape, color, brightness, and size, with a pre-trained classifier to determine if they are gas bubbles or impurities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional algorithms are used to classify foreign bodies, then the inspection process is simple and fast, but the accuracy of distinguishing between bubbles and impurities is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/optical inspection methods with a computer vision system that uses image processing algorithms and machine learning classifiers to automatically distinguish between bubbles and impurities, achieving higher measurement precision while managing system complexity through automated intelligence
Solution Approach 2:
The patent changes the classification approach by using multiple parameters including shape, size, brightness, color, and motion characteristics, transforming the inspection from simple binary classification to multi-parameter analysis that significantly improves distinction accuracy between bubbles and impurities
2Stability of the object's composition
If the container is rotated at high speed to agitate particles, then the liquid mixing is improved, but bubbles are formed during rotation causing false rejects
Solution Approach 1:
The patent performs preliminary classification of foreign bodies during the rotation phase before final inspection, using image capture and analysis to identify and exclude bubbles from impurity counting, thereby preventing false rejects while maintaining effective liquid mixing through the rotation agitation
Solution Approach 2:
The patent converts the harmful effect of bubble formation during high-speed rotation into a beneficial opportunity by capturing images during the rotation phase and using machine learning to identify bubbles, allowing the agitation to serve dual purposes: mixing and providing data for accurate classification
3Measurement precision
If traditional image capture methods are used, then the system is simple, but micro-bubbles and bubbles with different shapes cannot be detected with sufficient accuracy
Solution Approach 1:
The patent segments the foreign body detection task into multiple classification categories including micro-bubbles, regular bubbles, and impurities, using distinct feature sets for each type such as size thresholds, shape characteristics, and motion patterns, enabling accurate detection of micro-bubbles that would otherwise be indistinguishable
Solution Approach 2:
The patent adds temporal dimension to the image analysis by capturing multiple images at different time instants during container rotation, allowing the system to analyze motion characteristics and trajectory patterns that enable distinction between bubbles and impurities based on their different movement behaviors over time
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides high reliability in distinguishing between bubbles and impurities, reducing false rejects by accurately tracking and classifying foreign bodies, ensuring only impurities are counted as contaminants, thus improving product quality assurance.
Implementation Method 1
the container with the liquid therein is rotated at a relatively high speed so as to agitate the particles in the liquid
Implementation Method 2
the container is then stopped suddenly so that the liquid inside the container continues moving
Implementation Method 3
bubbles with different shapes from the conventional shape of a bubble due to refraction phenomena
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A method, implemented by a computer, for calculating a parameter representing the impurities in a liquid product inside a container (3), comprising the steps of receiving a plurality of images of at least one portion of the liquid in the container (3), captured at different time instants; and analysing the images to identify foreign bodies in the liquid and determine whether each foreign body corresponds to a gas bubble. The method further comprises the steps of recognizing the same foreign body in different images so as to define foreign body sequences, counting the number of foreign bodies detected excluding the foreign body sequences the foreign bodies of which have been recognized as gas bubbles in at least one image, and finally determining the parameter representing the impurities in the liquid product based on the count.