In this thesis we present a computer vision system for the crop optimization of
hot rolled steel plates in a hot rolling mill. The system is meant to demonstrate
the workings of such a system and is used as a tool for operators in the decision
making process.
In the thesis, we first define the problem and working requirements of the
system. We also show an overview of similar works in the field and describe
some segmentation methods. Next, we give the basic theoretical knowledge of
the used computer vision and convolutional neural network methods, necessary
for understanding the workings of the system. The hardware, finished system
and its training on real process data is shown next. In the last part, we present
the final operation of the system and its performance. The performance is shown
in two parts. In the first part, we present the performance of the segmentation
model that is vital for the overall good operation of the whole system. In the
second part, we show the final performance of the whole system.
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