Real-time Product Quality Inspection Monitoring System using Quadratic Distance and Level Classifier

Authors

  • Norhashimah Mohd Saad Center for Robotics and Industrial Automation, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Nor Nabilah Syazana Abdul Rahman Center for Robotics and Industrial Automation, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Abdul Rahim Abdullah Center for Robotics and Industrial Automation, Faculty of Electric Engineering, Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Norhizam Abdul Rahim Stevia Industry Sdn. Bhd., Kawasan Perindustrian KEDA, 08300 Gurun, Kedah, Malaysia

Keywords:

Color classification, Level classification, Product quality inspection, Quadratic distance classifier,

Abstract

Automated product quality inspection has become a very important process in industries to maintain high product efficiency. This paper presents a real-time product quality inspection monitoring system for beverages product. The proposed system used Internet Protocol (IP) camera to capture the image of the bottle through computer network in order to inspect color concentration and water level of the bottle. Quadratic distance technique is applied for color concentration analysis based on a combination of Red, Green and Blue (RGB) histogram. The vertical and horizontal coordinates technique is used to inspect three conditions of the level, which are passed, overfill and underfill. The proposed system has achieved 100% accuracy using 246 samples.

References

Huang, S.H. and Pan, Y.C., 2015. Automated visual inspection in the semiconductor industry: A survey. Computers in industry, 66, pp.1-10.

Rababaah, A.R. and Demi-Ejegi, Y., 2012, May. Automatic visual inspection system for stamped sheet metals (AVIS 3 M). In Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference on (Vol. 2, pp. 661-665). IEEE.

Bama, B.S., Valli, S.M., Raju, S. and Kumar, V.A., 2011. Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian Journal of Computer Science and Engineering, 2(2), pp.202-211.

Zheng, D., Song, W., Dai, Z. and Wang, H., 2014, May. The Objectifying System Using for Color Inspection of Traditional Chinese Medicine Based on the Digital Image Technology. In Medical Biometrics, 2014 International Conference on (pp. 21-25). IEEE.

Dave, V.A., Hadia, S.K., Dave, V.A. and Hadia, S.K., Liquid level and cap closure united inspection using image processing. International Journal, 1, pp.62-68. Kreutzer, J.F., Flaschberger, J., Hein, C.M. and Lueth, T.C., 2016, June. Capacitive detection of filling levels in a cup. In Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13th International Conference on (pp. 31-36). IEEE.

Pithadiya, K.J., Modi, C.K. and Chauhan, J.D., 2010, February. Machine vision based liquid level inspection system using ISEF edge detection technique. In Proceedings of the International Conference and Workshop on Emerging Trends in Technology (pp. 601-605). ACM.

Hutabarat, D.P., Patria, D., Budijono, S. and Saleh, R., 2016, October. Human tracking application in a certain closed area using RFID sensors and IP camera. In Information Technology, Computer, and Electrical Engineering (ICITACEE), 2016 3rd International Conference on (pp. 11-16). IEEE.

Arthur, D., Biggs, E., Hagerman, P., McGaha, D. and Pratt, M.J., Sprint Communications Company LP, 2016. Network-attached storage solution for application servers. U.S. Patent 9,509,718.

Wu Chun-Ling. The research on inspection system of mechanical parts number based on computer vision[J]. The modern produce engineering, 2006,13(4):101-103 A. S. Prabuwono, R. Sulaiman, A. R. Hamdan, and Hasniaty A., “Development of intelligent visual inspection system (IVIS) for bottling machine”, in Proc. IEEE Tencon'07, 2007.

Mohd Saad, N. and Abdullah, A.R., 2011. Brain Lesion segmentation from diffusion weighted MRI based on adaptive thresholding and gray level co-occurrence matrix. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), pp.1-13.

Junhua, C. and Jing, L., 2012, December. Research on color image classification based on HSV color space. In Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2012 Second International Conference on (pp. 944-947). IEEE.

Wang, X. and Xue, Y., 2016, June. Fast HEVC intra coding algorithm based on Otsu's method and gradient. In Broadband Multimedia Systems and Broadcasting (BMSB), 2016 IEEE International Symposium on (pp. 1-5). IEEE.

Sehgal, S., Kumar, S. and Bindu, M.H., 2017, January. Remotely sensed image thresholding using OTSU & differential evolution approach. In Cloud Computing, Data Science & EngineeringConfluence, 2017 7th International Conference on (pp. 138-142). IEEE.

Zheng, Y., 2016. A positive definite quadratic programming algorithm based on distance. Journal of Interdisciplinary Mathematics, 19(2), pp.301-310.

Panetta, K.A., Nercessian, S. and Agaian, S., Trustees Of Tufts College, 2016. Methods and apparatus for image processing and analysis. U.S. Patent 9,299,130.

Downloads

Published

2017-09-27

How to Cite

Mohd Saad, N., Abdul Rahman, N. N. S., Abdullah, A. R., & Abdul Rahim, N. (2017). Real-time Product Quality Inspection Monitoring System using Quadratic Distance and Level Classifier. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-13), 57–62. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2566