An intelligent classification system for aggregate based on image processing and neural network
Loading...
Date
2009
Authors
Mohammad Al-Batah, Mohammad Subhi
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Aggregate’s shape and surface texture immensely influence the strength and structure of
the resulting concrete. Traditionally, mechanical sieving and manual gauging are used
to determine both the size and shape of the aggregates. These methods, which are often
performed manually, tend to be slow, highly subjective and laborious. Therefore, in this
research, an intelligent classification system consisting of the Automatic Features
Extraction (AFE) algorithm and the intelligent Neural Network classification for
aggregate recognition is designed and developed. The proposed AFE algorithm is
capable of automatically extracting the Hu, Zernike and Affine moments from the
segmented object. Then, three feature selection techniques namely the Discriminant
Analysis (DA), Principal Component Analysis (PCA) and Circle Segments (CS) are
employed to identify the useful and important features of aggregate for its classification.
In this study, a new learning algorithm called Modified Recursive Least Squares
(MRLS) is introduced for the Hybrid Multilayered Perceptron (HMLP) network. The
effectiveness of the MRLS algorithm is demonstrated using several benchmark data sets.
Additionally, two new Artificial Neural Network architectures are proposed to improve
the performance of the MLP and HMLP network in handling aggregate classification
problem. The first architecture is called Cascaded MLP (c-MLP) and the second
architecture is called Hierarchical HMLP (H2MLP) network, respectively. Finally, an
automatic intelligent aggregate classification system called AutoAgg is developed. The
system contains the modified AFE algorithm as automatic features extraction and the
H2MLP network trained with MRLS algorithm as intelligent classification. The system
is capable of automatically capturing the image, extracting the features and classifying
the aggregate in less than one second. Here, the system classifies the aggregates into six
shapes with an accuracy of 99% based upon 4242 tested aggregate images.
Description
PhD
Keywords
Electrical engineering , Intelligent classification system , Image processing and , Neural network