JST  Vol.3 No.4 , December 2013
Automated Visual Inspection System for Specifying Brick Quality
Automated visual inspection system has been developed to specify brick quality and the accepting of the bricks in a production line. This system is based on CMOS web-camera placed in manufacture line. Depending on diameters, area, perimeter and cracks of a brick, a strong algorithm has been developed, and this algorithm is created to befit the required for measuring bricks quality. The quality is measured by fuzzy system which can give percent accepting to a brick under the test. Fuzzy reasoning gives the system more reliability than other inspection system.

Cite this paper
A. Marhoon, A. Younis and F. Taha, "Automated Visual Inspection System for Specifying Brick Quality," Journal of Sensor Technology, Vol. 3 No. 4, 2013, pp. 110-114. doi: 10.4236/jst.2013.34017.
[1]   F. You and Y. Zhang, “A Mechanical Part Sorting System Based on Computer Vision,” IEEE International Conference on Computer Science, 2008, pp. 860-863.

[2]   H. Akbar and A. S. Prabuwono, “Webcam Based System for Press Part Industrial Inspection Faculty of Information and Communication Technology,” Technical University of Malaysia Melaka Locked Bag 1200, Hang Tuah Jaya, Ayer Keroh, Melaka.

[3]   J. C. Noordam, G. W. Otten, A. J. M. Timmermans and B. H. van Zwol, “High Speed Potato Grading and Quality Inspection Based on a Color Vision System,” Department Production & Control Systems, ATO, Wageningen.

[4]   J. Blasco, N. Aleixos, S. Cubero, F. Juste, J. Gómez-Sanchis, V. Alegre and E. Moltó, “Computer Vision Developments for the Automatic Inspection of Fresh and Processed Fruits,” Centro de Agroingeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA). Ctra. Moncada-Náquera km 5, 46113 Moncada (Valencia), Spain. Instituto en Bioingeniería y Tecnología Orientada al Ser Humano (Universidad Politécnica de Valencia). Camino de Vera s/n, 46022 Valencia, Electronic Engineering Department, Universidad de Valencia, Dr. Moliner 50, Burjassot, Valencia, Bornimer Agrartechnische Berichte Heft 69, 2011.

[5]   T. Brosnan and D.-W. Sun, “Improving Quality Inspection of Food Products by Computer Vision,” FRCFT Group, Department of Agricultural and Food Engineering, University College Dublin, National University of Ireland, Earlsfort Terrace, Dublin 2, Journal of Food Engineering, Vol. 61, 2004, pp. 3-16. http://dx.doi.org/10.1016/S0260-8774(03)00183-3

[6]   L. Reznik, “Fuzzy Controllers,” Victoria University of Technology, Melbourne, 1997.