Wire connector classification with machine vision and a novel hybrid SVM

Vedang Chauhan; Keyur D. Joshi; Brian W. Surgenor

doi:10.1117/12.2309556

13 April 2018 Wire connector classification with machine vision and a novel hybrid SVM

Vedang Chauhan, Keyur D. Joshi, Brian W. Surgenor

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961J (2018) https://doi.org/10.1117/12.2309556
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.

Citation Download Citation

Vedang Chauhan, Keyur D. Joshi, and Brian W. Surgenor "Wire connector classification with machine vision and a novel hybrid SVM", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961J (13 April 2018); https://doi.org/10.1117/12.2309556

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available