Optimal Illumination to Reveal Surface Irregularities



Sin, Tze Kiat (2019) Optimal Illumination to Reveal Surface Irregularities. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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All vision system requires quality images which will result in reliable and repeatable performance. The quality of the image acquired from vision application is highly dependent on the lighting configurations which are the angle, color and amount of lighting used to illuminate an object. Machine vision lighting should maximize feature contrast while minimizing contrast of the rest, thereby allowing the camera to seen the part or mark clearly. This research are aim to evaluate the optimum illumination environment to reveal surface irregularities, select an optimum illumination to reveal surface irregularities, develop a program that can detect surface irregularities and design best position for both the set-up of camera and lighting for best output. Transparent, Curve and Flat surfaces are inspected using different lightings and set-ups. After that, the images captured are going through machine vision inspection system which is coded using MATLAB. The algorithms used in this system are mainly on edge detection algorithm and pattern matching algorithm. Then, the best lightings and their optimal lightings set-ups are selected based on the best image captured for machine vision inspection. The optimum lighting environments for transparent, curve and flat surfaces are evaluated and tabulated in this research. In a nutshell, lightings are extremely important in vision system. A proper lighting environment will reduce lot of development time and system processing time. Hence, based on the object and surface to be inspected, a good lighting shall be chosen.

Item Type: Final Year Project
Subjects: Technology > Electrical engineering. Electronics engineering
Faculties: Faculty of Engineering > Bachelor of Engineering (Honours) Electrical and Electronics
Depositing User: Library Staff
Date Deposited: 07 Feb 2020 09:23
Last Modified: 11 Apr 2022 04:56
URI: https://eprints.tarc.edu.my/id/eprint/13071