The Internet of Things Beverages Bottle Shape Defect Detection using Naïve Bayes Classifier

N.N.S. Abdul Rahman, N.M. Saad, A. R. Abdullah, F. Abdul Wahab

Abstract


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.


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