Cigarette Fly Ash Detection via Machine Vision Gray-Scale Analysis
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Solution Overview
Problem
Current methods for evaluating cigarette ash cohesion performance are static and do not effectively assess cigarette fly ash during smoking, which can lead to environmental pollution and consumer dissatisfaction.
Innovation Solution
A device and method using machine vision to detect cigarette fly ash by tracking the burning state of a cigarette in real time, utilizing a manipulator, image acquisition, and image processing to quantify fly ash through gray-scale differences in images, simulating human smoking actions to accurately measure ash falling from the ash column.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static burning evaluation method is used, then the evaluation process is simple, but it cannot objectively reflect the falling ash and fly ash during smoking
Solution Approach 1:
The patent replaces manual mechanical detection with an automated machine vision system. The image acquisition device captures burning states, and the image processing device automatically analyzes fly ash through gray-scale difference algorithms, eliminating the need for manual observation and evaluation.
Solution Approach 2:
The patent uses image copying to create digital replicas of the burning cigarette states. By capturing multiple images during the burning process and comparing them through gray-scale analysis, the system creates a digital record that can be objectively measured and analyzed without interfering with the actual burning process.
2Measurement precision
If manual detection method is used, then the device complexity is low, but it is difficult to detect cigarette fly ash due to short duration and high requirements for accuracy
Solution Approach 1:
The patent implements continuous image acquisition during the entire burning process. The image acquisition device continuously captures the burning state at multiple time points, ensuring that fly ash events are not missed due to their short duration. This continuous monitoring approach maintains high detection accuracy while improving efficiency through automated processing.
Solution Approach 2:
The image processing device provides feedback by automatically analyzing captured images and determining fly ash presence through gray-scale difference calculations. This feedback mechanism enables real-time detection and quantification of fly ash, allowing for immediate evaluation without manual intervention and significantly improving detection efficiency.
3Measurement precision
If real-time tracking of burning state is implemented, then fly ash detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct functional modules: the image acquisition device for capturing burning states, and the image processing device for analyzing the captured images. This segmentation allows each module to be optimized independently while working together to achieve accurate real-time tracking of the burning state and fly ash detection.
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
Provides an objective and accurate characterization of cigarette fly ash, overcoming the limitations of static evaluation methods, ensuring improved ash cohesion performance and quality optimization.
Implementation Method 1
the image acquisition device is configured to synchronously acquire images of burning ash columns of the cigarette during the simulated smoking process
Implementation Method 2
determine whether fly ash appears by a gray-scale difference of the burning ash columns of the cigarette in the images
Data Source
AI summary
A device and a method for detecting cigarette fly ash by a gray-scale difference based on machine vision (MV) are provided. A manipulator holds a cigarette as a detection sample to simulate a human smoking action, and multiple groups of cameras track a simulated smoking process of the detection sample synchronously in real time. It is determined whether fly ash appears based on a gray-scale difference of burning ash columns, produced without being subjected to flicking, in acquired images. A fly ash area of the burning ash columns of the cigarette is calculated by an area with the gray-scale difference of the burning ash columns of the cigarette in two sequential images of the burning ash columns of the cigarette, and a fly ash amount is further determined. The detection of the amount of fine fly ash is converted into the detection of the gray-scale difference.