A Comparative Study of Machine Vision Classification Techniques for the Detection of Missing Clips

This thesis provides a comparative study of machine vision (MV) classification techniques for the detection of missing clips on an automotive part known as a cross car beam. This is a difficult application for an automated MV system because the inspection is conducted in an open manufacturing environment with variable lighting conditions. A laboratory test cell was first used to investigate the effect of lighting. QVision, a software program originally developed at Queen’s University, was used to perform a representative inspection task. Solutions with different light sources and camera settings were investigated in order to determine the best possible set up to acquire an image of the part. Feature selection was applied to improve the results of this classification. The MV system was then installed on an industrial assembly line. QVision was modified to detect the presence or absence of four clips and communicate this information to the computer controlling the manufacturing cell. Features were extracted from the image and then a neuro fuzzy (ANFIS) system was trained to perform the inspection. A performance goal of 0% False Positives and less than 2% False Negatives was achieved with the feature based ANFIS classifier. In addition, the problem of a rusty clip was examined and a radial hole algorithm was used to improve performance in this case. In this case, the system required hours to train. Five new classifiers were then compared to the original feature based ANFIS classifier: 1) feature based with a Neural Network, 2) feature based with principle component analysis (PCA) applied and ANFIS, 3) feature based with PCA applied and a Neural Network, 4) Eigenimage based with ANFIS and 5) Eigenimage based with a Neural Network. The effect of adding a Hough rectangle feature and a principle component colour feature was also studied. It was found that the Neural Network classifier performed better than the ANFIS classifier. When PCA was applied the results improved still further. Overall, feature based classifiers had better performance than Eigenimage based classifiers. Finally, it should be noted that these six classifiers required only minutes to train.

Department dc:contributor.department

Mechanical and Materials Engineering

Year dc:date.issued

2009

Author dc:creator

• Miles, Brandon

Advisor dc:contributor.supervisor

• Surgenor, Brian W.

• Machine Vision
• Image Processing

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1974/2592

OAI identifier oai:identifier

oai:queensu.scholaris.ca:1974/2592