The Internet of Things Beverages Bottle Shape Defect Detection using Naïve Bayes Classifier
This paper presents an automated
computer vision system using internet of things
(IoT) platform for shape defect detection. The
proposed system used beverage bottles as a tested
product. Morphological operation is applied to
segment the image using erosion and dilation
process. The features of shape bottle such as
area, perimeter, major axis length and extend are
extracted. Naïve Bayes classifier is implemented
to classify the shape of bottle either pass or rejects
based on the estimated extend parameters. All the
images are taken using webcam and the captured
image is stored in server for wirelessly access.
The analysis is done by using image processing
toolbox using Matlab in real-time. The result
demonstrate that the tested product based on
shape is achieved 92% accuracy for good bottle and
90% accuracy for defect bottle using 100 sample
images. It shows that the proposed system can be
applied for beverages quality control application.
N. M. Saad, N. Nabilah, and S. Abdul, “Shape
Defect Detection using Local Standard Deviation
and Rule-Based Classifier for Bottle Quality
Inspection,” Indones. J. Electr. Eng. Comput. Sci.,
vol. 8, no. 1, pp. 107–114, 2017.
A. Kujawińska and K. Vogt, “Human factors in
visual quality control,” Manag. Prod. Eng. Rev.,
vol. 6, no. 2, pp. 25–31, 2015.
G. Moradi, M. Shamsi, M. H. Sedaaghi, and S.
Moradi, “Apple defect detection using statistical
histogram based Fuzzy C-means algorithm,”
Electr. Eng. IEEE, pp. 11–15, 2011.
X. Liu, Y. Yang, M. Gao, J. Huang, and Z. He,
“A machine vision system for film capacitor
defect inspection,” Proc. 2015 10th IEEE Conf.
Ind. Electron. Appl. ICIEA 2015, pp. 1414–1419,
H. M. Haniff, M. Sulaiman, H. N. M. Shah, and
L. W. Teck, “Shape-Based Matching: Defect
Inspection of Glue Process in Vision System,”
IEEE Symp. Ind. Electron. Appl. ISIEA 2011,
pp. 53–57, 2011.
H. Mu, D. Qi, M. Zhang, and P. Zhang, “Study
of wood defects detection based on image
processing,” Proc. - 2010, 7th Int. Conf. Fuzzy Syst.
Knowl. Discov. FSKD 2010, vol. 2, no. Fskd, pp.
S. Ramli, M. M. Mustafa, A. Hussain, and D. A.
Wahab, “Plastic Bottle Shape Classification Using
Partial Erosion-based Approach,” Mod. Appl. Sci.,
vol. 6, no. 4, pp. 77–83, 2012.
X. Wang and Y. Xue, “Fast HEVC Intra Coding
Algorithm Based on Otsu’s Method and
H. Liu, Y. Wang, and F. Duan, “An Empty Bottle
Intelligent Inspector Based on Support Vector
Machines and Fuzzy Theory,” Proc. World Congr.
Intell. Control Autom., vol. 2, pp. 9739–9743, 2006.
H. A. Abdul Salam, M. H. Taha, N. M. Sahib,
and H. F. Ali, “Improved approach to iris
segmentation based on brightness correction
for iris recognition system,” J. Theor. Appl. Inf.
Technol., vol. 95, no. 23, pp. 6410–6418, 2017.
S. B. Kotsiantis, “Supervised Machine Learning:
A Review of Classification Techniques,”
Informatica, vol. 31, pp. 249–268, 2007.
J. Chen, H. Huang, S. Tian, and Y. Qu, “Feature
selection for text classification with Naïve Bayes,”
Expert Syst. Appl., vol. 36, no. 3 PART 1, pp. 5432–
N. Hossain, M. T. Kabir, T. R. Rahman, M.
S. Hossen, and F. Salauddin, “A real-time
surveillance mini-rover based on OpenCVPython-
JAVA using Raspberry Pi 2,” Proc. - 5th
IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE
, no. November, pp. 476–481, 2016.
H. Guerra, A. Cardoso, V. Sousa, J. Leitao, V.
Graveto, and L. M. Gomes, “Demonstration of
Programming in Python using a Remote Lab
with Raspberry Pi,” Exp. Int. Conf. (exp. at’15),
3rd (pp. 101-102). IEEE., pp. 101–102, 2015.
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