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Chapter 10
10.
Conclusion and Outlook
The Camera and the image processing unit belong the very important parts of machine
vision-based ADAS concepts. Different weather and environmental conditions can
significantly influence the image processing performance. The key challenging issue in
ADAS is safety; hence all part of the system should work under any conditions without
providing wrong information. To have a robust and real-time image processing module for
ADAS, we do need a high performance processing system. Computing huge amount of
visual information for extracting meaningful data, features, etc needs a special/appropriate
hardware and processing architecture. In this research we did a survey about different
hardware platforms for image processing and according to real-time related ADAS
requirements we have proposed a robust and real-time architecture based on CNN, FPGA
and GPU. The whole the system is a CNN based processing which is developed
implemented on either GPU or FPGA. Before CNN implementation, we tried to understand
the theory of analog computing and developed a direct emulation of that paradigm on the
FPGA. Since there are many similarities between a CNN implementation and analog
computing based on DDA, we could find a good way to implement CNN on FPGA. In this
thesis, we have shown that CNN can solve a series of image processing tasks in real-time.
This thesis has formulated 7 key research questions and each main chapter did provide an
answer to a respective research question:
