Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Industrial Engineering, Department of Industrial Engineering, Durban University of Technology, 2009. / This research focuses on the design and implementation of an intelligent machine vision and
sorting system that can be used to sort objects in an industrial environment. Machine vision
systems used for sorting are either geometry driven or are based on the textural components of an
object’s image. The vision system proposed in this research is based on the textural analysis of
pixel content and uses an artificial neural network to perform the recognition task. The neural
network has been chosen over other methods such as fuzzy logic and support vector machines
because of its relative simplicity. A Bluetooth communication link facilitates the communication
between the main computer housing the intelligent recognition system and the remote robot
control computer located in a plant environment. Digital images of the workpiece are first
compressed before the feature vectors are extracted using principal component analysis. The
compressed data containing the feature vectors is transmitted via the Bluetooth channel to the
remote control computer for recognition by the neural network. The network performs the
recognition function and transmits a control signal to the robot control computer which guides
the robot arm to place the object in an allocated position.
The performance of the proposed intelligent vision and sorting system is tested under different
conditions and the most attractive aspect of the design is its simplicity. The ability of the system
to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced
with missing data made the neural network an attractive option over fuzzy logic and support
vector machines.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:dut/oai:ir.dut.ac.za:10321/494 |
| Date | January 2009 |
| Creators | Li, Zhi |
| Contributors | Govender, Poobalan |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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