wsq/polarrcnn-thesis
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@ -224,25 +224,28 @@ In recent years, fueled by advancements in deep learning and the availability of
\begin{subfigure}{\subwidth}
\includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_nms/all2_gt.jpg}
\caption{GT}
\end{subfigure}
\begin{subfigure}{\subwidth}
\includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_nms/all2_pred50.jpg}
\caption{NMS50}
\end{subfigure}
\begin{subfigure}{\subwidth}
\includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_nms/all2_pred15.jpg}
\caption{NMS15}
\end{subfigure}
\begin{subfigure}{\subwidth}
\includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_nms/all2_nmsfree.jpg}
\caption{NMSFree}
\end{subfigure}
\vspace{0.5em}
\caption{hhh}
\end{figure*}
\end{figure*}
\begin{figure*}[htbp]
\begin{figure*}[htbp]
\centering
\def\subwidth{0.24\textwidth}
\def\imgwidth{\linewidth}
@ -339,9 +342,8 @@ In recent years, fueled by advancements in deep learning and the availability of
\end{subfigure}
\vspace{0.5em}
\caption{hhh}
\end{figure*}
\end{figure*}
Drawing inspiration from object detection methods such as Yolos and Fast RCNN, several anchor-based approaches have been introduced for lane detection, the representative work including LanesATT and CLRNet. These methods have demonstrated superior performance by leveraging anchor priors and enabling larger receptive fields for feature extraction. However, anchor-based methods encounter similar drawbacks as anchor-based general object detection method as follows:
@ -399,18 +401,18 @@ Lanes are thin and long curves, a suitable lane prior helps the model to extract
\textbf{Polar Coordinate system.} Since the lane anchor are set to be straight by default, it could be described by the straight line parameter. Previous work uses a ray to describe a 2D line anchor, and the parameters of a ray contain the start point's coordinates and the orientation/angle, i.e., $\left\{\theta, P_{xy}\right\}$, as shown in Figure \ref{coord} (a). \cite{}\cite{} define the start points locates on the three image boundary. And \cite{} points out that this not reasonable because the real start point of a lane could be in any location within an image. In our analysis, using a ray may cause ambiguity in describing a line because a line may have infinite start points and the start point of the lane is subjective. As illustrated in Figure \ref{coord} (a), the yellow and darkgreen start points with the same orientation $\theta$ describe the same line, and either of them could be chosen in different datasets. This ambiguity arises because a straight line has two degrees of freedom while a ray has three degrees of freedom. To address this issue, as shown in Figure \ref{coord} (b), we use polar coordinate systems to describe a lane anchor with two parameters for radius and angle $\left\{\theta, r\right\}$, where $\theta \in \left[-\frac{\pi}{2}, \frac{\pi}{2}\right)$ and $r \in \left(-\infty, +\infty\right)$.
\begin{figure}[t]
\centering
\def\subwidth{0.24\textwidth}
\def\imgwidth{\linewidth}
\def\imgheight{0.4\linewidth}
\begin{subfigure}{\subwidth}
\centering
\includegraphics[width=1\linewidth]{thsis_figure/coord/ray.png}
\includegraphics[width=\imgwidth]{thsis_figure/coord/ray.png}
\caption{}
\end{subfigure}
\hfill
\begin{subfigure}{\subwidth}
\centering
\includegraphics[width=1\linewidth]{thsis_figure/coord/polar.png}
\includegraphics[width=\imgwidth]{thsis_figure/coord/polar.png}
\caption{}
\end{subfigure}
\caption{Different descriptions for anchor parameters. (a) Ray: start point and orientation. (b) polar: radius and angle.}
@ -460,7 +462,7 @@ where $BCE\left( \cdot , \cdot \right) $ denotes the binary cross entropy loss a
\begin{figure}[t]
\centering
\includegraphics[width=0.48\textwidth]{thsis_figure/coord/localpolar.png}
\includegraphics[width=\linewidth]{thsis_figure/coord/localpolar.png}
\caption{Label construction for local polar proposal module.}
\label{lphlabel}
\end{figure}
@ -490,21 +492,21 @@ Then the feature points can be sample on the line anchor. The y coordinate of po
\begin{figure}[t]
\centering
\includegraphics[width=0.49\textwidth]{thsis_figure/triplet_head.png} % 替换为你的图片文件名
\includegraphics[width=\linewidth]{thsis_figure/triplet_head.png} % 替换为你的图片文件名
\caption{The main architecture of global head}
\label{triplet}
\end{figure}
\begin{figure}[t]
\centering
\includegraphics[width=0.4\textwidth]{thsis_figure/gnn.png} % 替换为你的图片文件名
\includegraphics[width=\linewidth]{thsis_figure/gnn.png} % 替换为你的图片文件名
\caption{The main architecture of our model.}
\label{gnn}
\end{figure}
\begin{figure}[t]
\centering
\includegraphics[width=0.49\textwidth]{thsis_figure/GLaneIoU.png} % 替换为你的图片文件名
\includegraphics[width=\linewidth]{thsis_figure/GLaneIoU.png} % 替换为你的图片文件名
\caption{Illustrations of GLaneIoU re-defined in our work.}
\label{glaneiou}
\end{figure}
@ -529,7 +531,7 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
\begin{figure}[t]
\centering
\includegraphics[width=0.48\textwidth]{thsis_figure/anchor_num_method.png}
\includegraphics[width=\linewidth]{thsis_figure/anchor_num_method.png}
\caption{Anchor Number and f1-score of different methods on CULane.}
\label{anchor_num_method}
\end{figure}
@ -559,7 +561,7 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
\begin{table*}[htbp]
\centering
\caption{Dataset \& preprocess}
\begin{adjustbox}{width=0.9\linewidth}
\begin{adjustbox}{width=\linewidth}
\begin{tabular}{l|l|ccccc}
\toprule
\multicolumn{2}{c|}{\textbf{Dataset}} & CULane & TUSimple & LLAMAS & DL-Rail & Curvelanes \\