Related Experiment Video
Updated: Jan 16, 2026
13:30
Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics
Published on: February 18, 2022
4.9K
Application of python image analysis tools for particle structure detachment detection in high‑speed videos during
Ole Desens1, Jörg Meyer1, Achim Dittler1
1Karlsruhe Institute of Technology (KIT), Institute of Mechanical Process Engineering and Mechanics (MVM) - Gas Particle Systems, Straße am Forum 8, 76131 Karlsruhe, Germany.
Methodsx
|October 1, 2025
Summary
A new Python workflow detects particle detachments during filter regeneration, crucial for air quality. This semi-automated method enhances monitoring of combustion engine emissions.
Area of Science:
- Combustion engine emissions control
- Particulate matter analysis
- Image processing for environmental science
Background:
- Combustion engines produce particulate emissions, impacting air quality and regulatory compliance.
- Wall-flow particulate filters capture soot, requiring regeneration to maintain performance.
- Soot detachment during regeneration can lead to downstream particle transport.
Purpose of the Study:
- To develop and validate a Python-based image analysis workflow for detecting particle structure detachments.
- To semi-automate the detection and verification process of soot detachments during filter regeneration.
- To improve the understanding of particle behavior within filters during regeneration.
Main Methods:
- A two-module Python workflow utilizing OpenCV and NumPy for image analysis.
- Module 1: Background subtraction (MOG2) and morphological operations for candidate structure identification.
- Module 2: Thresholding and pixel-wise difference mapping for detachment verification in regions of interest.
Main Results:
- Successfully detected six small detachment events (100-300 µm) in a 796,000-frame dataset.
- The workflow demonstrated reliable detection of detachment events.
- Reduced manual review time for analyzing high-speed video recordings.
Conclusions:
- The presented Python workflow offers an effective semi-automated solution for detecting particle detachments during filter regeneration.
- This method aids in monitoring filter performance and understanding emission dynamics.
- The validated workflow can be applied to high-speed video analysis of particulate filter regeneration.
