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Add requirements section in prototype design

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Tobias Eidelpes 2023-11-23 17:10:27 +01:00
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@ -1962,9 +1962,6 @@ from them.
\label{chap:design}
\begin{enumerate}
\item Expand on the requirements of the prototype from what is stated
in the motivation and problem statement. (Two-stage approach, small
device, camera attached, outputs via REST API)
\item Describe the architecture of the prototype (two-stage approach
and how it is implemented with an object detector and
classifier). How the individual stages are connected (object
@ -1983,16 +1980,35 @@ Estimated 10 pages for this chapter.
\section{Requirements}
\label{sec:requirements}
Briefly mention the requirements for the prototype:
The basic requirements for the prototype have been introduced in
section~\ref{sec:motivation} and stem from the research questions
defined in the same section. The aim of this work is to detect
household plants, classify them into water-stressed or healthy, and to
continuously publish the results via a \gls{rest} \gls{api}. To this
end, a portable \gls{sbc} such as the Nvidia Jetson Nano stores the
trained models locally and uses them for inference on images which are
periodically taken with an attached camera.
\begin{enumerate}
\item Detect household potted plants and outdoor plants.
\item Classify plants into stressed and healthy.
\item Camera attached to device.
\item Deploy models to device and perform inference on it.
\end{enumerate}
The prototype is thus required to be running the models on its own
without help from a central server or other computational
resource. However, because the results are published via a \gls{rest}
service, internet access is necessary to be able to retrieve the
predictions.
Estimated 1 page for this section.
Other functional requirements are that the inference on the device for
both models does not take too long (i.e. not longer than a few seconds
per image). Even though plants are not known to grow extremely rapidly
from one minute to the next, keeping the inference time low results in
a more resource efficient prototype. As such, it is possible to run
the device off of a battery which completes the self-contained nature
of the prototype.
From an evaluation perspective, the models are required to attain a
reasonable level of accuracy. It is difficult to determine said level
beforehand, but considering the task as well as general object
detection and classification benchmarks such as \gls{coco}
\cite{lin2015}, we expect a \gls{map} of around 40\% and precision and
recall values of 70\%.
\section{Design}
\label{sec:design}