Particle Inspection

Particle Inspection: Modules of the Visual Particle Inspection Subsystem; Detection of Particle Contamination in Medicine Containers with Novel Solutions for Background Subtraction and Segmentation, Classification, and Tracking

van Eeden, Simon (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics)

Wisse, M. (mentor)
Caesar, H.C. (graduation committee)
Lentsch, T. (graduation committee)
van der Horst, Joren (graduation committee)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

2023-07-05

Visual inspection of liquid medicine containers for contamination and defects is mandatory and crucial to ensure their safety for injection. This document presents research and development of three modules of the Visual Particle Inspection Subsystem (VPIS), an automatic inspection subsystem with the task of detecting and classifying particle contamination in liquid medicine containers. The proposed VPIS comprises three modules for which research and development is performed separately: the backgrounds subtraction and segmentation module, the classification module, and the tracking module.

For the background subtraction and segmentation module, a solution is proposed with filtered temporal background modelling and locally adaptive threshold segmentation. This solution works on the assumption that moving objects such as particles and bubbles are sparse and do not occur more than twice in a pixel in twenty frames. Therefore, a representative incomplete background cluster can be obtained by removing the two lowest or highest pixel values depending on the mode of illumination. Next, the average value of the background cluster is used for background subtraction and the standard deviation of the background cluster is used to determine a locally adaptive segmentation threshold. The solution has been positively evaluated for detection of low contrast objects, insensitivity to disturbances, processing time, and parameter configuration. Additionally, an add-on solution is proposed that makes it possible to detect objects in the challenging region near the rubber stopper of a syringe.

For the classification module, out of four candidate classification methods a Convolutional Neural Network (CNN) is chosen for the classification of single object detections. The CNN classifier achieves an accuracy of 0.93 and is used as a baseline classifier for the rest of this research. Next, with three exploratory research questions, research is performed into opportunities and pitfalls for this classification problem summarised below.
-It is found that certain handcrafted features correlate to the classification accuracy of a detection. A method is proposed to predict the classification accuracy of detections and filter out detections that cannot be classified reliably. Compared simple filters that filter out detections based on a single feature such as area or total contrast, the proposed method should achieve a higher subsequent classification accuracy and reject less detections.
-With the simplest classification strategy that is often used for inspection, a container will be rejected if a single detection is classified as a particle. It is shown through simulation that this classification strategy is not effective for this classification problem. This is as the large number of bubble detections in a clean container would result in a high false container rejection rate.
-Two multi-detection classification strategies are proposed that use multiple detections for classification. Median voting classification uses results from the tracking module to classify multiple detections of the same object. Multi-positive classification does not use tracking results but requires multiple detections to be classified as particles. Simulation results indicate that with both strategies, the classification accuracy for containers can be drastically improved. It is theorized that the best results can be achieved with median voting classification...

Visual inspection
Medicine inspection
Computer vision
Background subtraction
Segmentation
Classification
Particle Tracking
TDCP

http://resolver.tudelft.nl/uuid:27f3a94b-2ef2-4a79-97b5-fba69f6a70cc

Student theses

master thesis

© 2023 Simon van Eeden