Application of python image analysis tools for particle structure detachment detection in high‑speed videos during model filter regeneration

Combustion-related particulate emissions are a challenge to air quality and regulatory compliance. In modern combustion engines, wall-flow particulate filters effectively capture soot particles, whereby periodic high-temperature (0₂) regeneration or passive (NO₂) regeneration is necessary to reduce the pressure drop. During regeneration, the soot layer breaks up, and small particle structures can detach and be transported further downstream towards the end of the filter channel. A Python-based image analysis workflow is presented for detecting and verifying particle structure detachments in high-speed video recordings of the filter regeneration. The method consists of two integrated modules using OpenCV and NumPy. In the first step, background subtraction (MOG2) and morphological operations are applied to identify candidate structures across video frames. The second step checks the particle structures detected in the first step, isolates a region of interest around the potential detachment and analyzes it using thresholding and pixel-wise difference mapping to confirm or reject the detachment event. Both modules allow parameters to be set and generate visual outputs for verification. The method was validated using a 796,000 frames dataset in which a model filter channel with carbon black loading was regenerated and six small detachment events (x_eq ≈ 100 - 300 µm) were detected.

• A Python-based method for detection of particle structure detachments in high‑speed videos of model filter regeneration.

• Semi-automated two-step detection and verification of detachments.

• Validated on 796 000 frames, reliably finding detachment events while reducing manual review time.