From 33c79a005e4953aafb58e0b2cb7c26e60d55d67a Mon Sep 17 00:00:00 2001
From: Tobias Eidelpes <tobias@eidelpes.info>
Date: Fri, 25 Aug 2023 18:09:35 +0200
Subject: [PATCH] Rework structure and content of background chapter

---
 thesis/thesis.tex | 78 ++++++++++++++++++++++++++++++++---------------
 1 file changed, 54 insertions(+), 24 deletions(-)

diff --git a/thesis/thesis.tex b/thesis/thesis.tex
index be88903..f3e5bf9 100644
--- a/thesis/thesis.tex
+++ b/thesis/thesis.tex
@@ -294,14 +294,62 @@ improvements.
 Describe the contents of this chapter.
 
 \begin{itemize}
-\item Related Work. (3 pages)
-\item Description of inner workings of YOLOv7 as the object detection
-  model. (4 pages)
-\item Description of inner workings of ResNet as the classification
-  model. (2 pages)
+\item Introduction to Object Detection, short ``history'' of methods,
+  region-based vs. single-shot, YOLOv7 structure and successive
+  improvements of previous versions. (10 pages)
+\item Introduction to Image Classification, short ``history'' of
+  methods, CNNs, problems with deeper network structures (vanishing
+  gradients, computational cost), methods to alleviate these problems
+  (alternative activation functions, normalization, residual
+  connections, different kernel sizes). (10 pages)
+\item Related Work. Add more approaches and cross-reference the used
+  networks with the theoretical sections on object detection and image
+  classification. (6 pages)
 \end{itemize}
 
-Estimated 9 pages for this chapter.
+Estimated 26 pages for this chapter.
+
+\section{Object Detection}
+\label{sec:background-detection}
+
+Give a definition of object detection and contrast it with instance
+segmentation/other detection tasks. Briefly mention how object
+detection was done before deep neural networks (feature-based methods
+(HOG, SIFT) and sliding window methods (Viola-Jones)). Go over the
+different approaches to object detection, namely region-based methods
+(Mask R-CNN and Faster R-CNN) and single-shot detection. Illustrate
+the approach region-based methods take and discuss problems arising
+from said approach (e.g. Dual-Priorities, multiple image passes and
+slow selective search algorithms for region proposals). Contrast the
+previous region-based methods with newer single-shot detectors such as
+YOLO and SSDnet. Describe the inner workings of the YOLOv7 model
+structure and contrast it with previous versions. What has changed and
+how did these improvements manifest themselves? Reference the original
+paper~\cite{wang2022} and papers of previous versions of the same
+model (YOLOv5~\cite{jocher2022}, YOLOv4~\cite{bochkovskiy2020}).
+
+Estimated 10 pages for this section.
+
+\section{Classification}
+\label{sec:background-classification}
+
+Give a definition of image classification and briefly mention the way
+in which classification was done before the advent of CNNs. Introduce
+CNNs, their overall design, and why a kernel-based approach allows
+two-dimensional data such as images to be efficiently processed. Give
+an introduction to SOTA classifiers before ResNet (AlexNet, VGGnet
+Inception/GoogLeNet), the prevailing opinion of \emph{going deeper}
+(stacking more layers) and the limit of said approach
+(\emph{Degradation Problem}) due to \emph{Vanishing
+Gradients}. Explain ways to deal with the vanishing gradients problem
+by using different activation functions other than Sigmoid (ReLU and
+leaky ReLU) as well as normalization techniques and residual
+connections. Introduce the approach of the \emph{ResNet} networks
+which implement residual connections to allow deeper layers. Describe
+the inner workings of the ResNet model structure. Reference the
+original paper~\cite{he2016}.
+
+Estimated 10 pages for this section.
 
 \section{Related Work}
 \label{sec:related-work}
@@ -440,24 +488,6 @@ sector. It is thus desirable to explore how plants other than crops
 show water stress and if there is additional information to be gained
 from them.
 
-\section{Object Detection}
-\label{sec:background-detection}
-
-Describe the inner workings of the YOLOv7 model structure. Reference
-the original paper~\cite{wang2022} and possibly papers of previous
-versions of the same model (YOLOv5~\cite{jocher2022},
-YOLOv4~\cite{bochkovskiy2020}).
-
-Estimated 4 pages for this section.
-
-\section{Classification}
-\label{sec:background-classification}
-
-Describe the inner workings of the ResNet model structure. Reference
-the original paper~\cite{he2016}.
-
-Estimated 2 pages for this section.
-
 \chapter{Prototype Development}
 \label{chap:development}