A robust machine vision system design to facilitate the automation of surface appearance inspections

Automotive manufacturers spend considerable sums on the painting process during manufacturing. Existing quality control methods using expert inspectors are generally effective in assessing perceived appearance; however, they are often labor intensive, time-consuming and can be subjective. An intelligent automated process that incorporates the effects of perception can facilitate an effective analysis of surface quality and appearance, in addition to maintaining optimal processing conditions for producing a desirable appearance. A long-term research goal is the development of a robust, automated in-line monitoring system which controls critical painting process parameters based in part upon the captured image data which correlates strongly with human visual assessment. This paper presents captured images and derived image attributes that correlate strongly with both objective measurements and a visual ranking of specular painted samples