Machine vision system for surface inspection on brushed industrial parts

Nicolas Bonnot; Ralph Seulin; Frederic Merienne

doi:10.1117/12.530683

3 May 2004 Machine vision system for surface inspection on brushed industrial parts

Nicolas Bonnot, Ralph Seulin, Frederic Merienne

Proceedings Volume 5303, Machine Vision Applications in Industrial Inspection XII; (2004) https://doi.org/10.1117/12.530683
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

This work aims at detecting defects on metallic industrial parts with streaked surface. The orientation of those parallel streaks is totally random. The searched defects are scratch and lack of machining. A specific machine vision system has been designed to deal with the particular inspected surface features. One image is acquired with an annular lighting in bright field and six images are acquired with a rotating lighting in dark field. A particular image processing is applied on the six images in order to get one image that represents all the revealed imperfections. A thresholding processing is then applied on this image in order to segment the imperfections. A trained classification, created with well known typical objects of each class, is performed. The classification has to recognize the different defects and the small imperfections that are not defects. The decision phase is used to know if the defects are acceptable, and therefore if the inspected part is acceptable. Some acceptability rules are defined for every defect class. The developed machine vision system has been implemented on an experimental industrial production line and it gives 2% of sub-detection and 16% of over-detection.

Citation Download Citation

Nicolas Bonnot, Ralph Seulin, and Frederic Merienne "Machine vision system for surface inspection on brushed industrial parts", Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); https://doi.org/10.1117/12.530683

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