update

main.tex

@@ -18,6 +18,8 @@
 \usepackage{booktabs}
 \usepackage{tikz}
 \usepackage[table,xcdraw]{xcolor}
+\usepackage[colorlinks,bookmarksopen,bookmarksnumbered, linkcolor=red]{hyperref}
+
 \definecolor{darkgreen}{RGB}{17,159,27} % 或者使用其他 RGB 值定义深绿色
 \aboverulesep=0pt
 \belowrulesep=0pt
@@ -45,6 +47,7 @@
 
 \begin{abstract}
     Lane detection is a critical and challenging task in autonomous driving, particularly in real-world scenarios where traffic lanes are often slender, lengthy, and partially obscured by other vehicles, complicating detection efforts. Existing anchor-based methods typically rely on prior straight line anchors to extract features and refine lane location and shape. Though achieving high performance, manually setting prior anchors is cumbersome, and ensuring sufficient anchor coverage across diverse datasets requires a large number of dense anchors. Furthermore, NMS postprocessing should be applied to supress the redundant predictions. In this study, we introduce PolarRCNN, a two-stage nms-free anchor-based method for lane detection. By introducing local polar head, the proposal of anchors are dynamic. The number of anchors are decreasing greatly without sacrificing performace.  What's more, a GNN based nms free head is proposed to enable the model reach an end-to-end format, which is deployment friendly. Our model yields competitive results on five popular lane detection benchmarks (Tusimple, CULane, LLAMAS, Curvelanes and DL-Rail) while maintaining a lightweight size and a simple structure.
+    Our source code are available at \href{https://github.com/ShqWW/PolarRCNN}{\textit{https://github.com/ShqWW/PolarRCNN}}.
 \end{abstract}
 \begin{IEEEkeywords}
 Lane detection.
@@ -109,7 +112,7 @@ In recent years, fueled by advancements in deep learning and the availability of
                 \caption{}
         \end{subfigure}
 
-        \caption{Comparision between different anchor thresholds in different scenarios. (a) Ground truth in dense scenario. (b) Predictions with large nms thresholds in dense scenario. (c) Ground truth in sparse scenario. (d) Predictions with small nms threshol in sparse scenario.}
+        \caption{Comparision between different anchor thresholds in different scenarios. (a) Ground truth in dense scenario. (b) Predictions with large nms thresholds in dense scenario. (c) Ground truth in sparse scenario. (d) Predictions with small nms threshold in sparse scenario.}
         \label{nms setting}
 \end{figure}
 
@@ -208,8 +211,6 @@ Dispired by the region proposal network in Faster RCNN \cite{}, the local polar
 
  The regression branch aim to proposed lane anchors by predicting the two parameters $F_{reg\,\,} \equiv \left[\mathbf{\Theta}^{H_{l} \times W_{l}}, \mathbf{\xi}^{H_{l}\times W_{l}}\right]$ under the local polar coordinate system, which denotes the angles and the radius. The classification branch predicts the heat map of the local polar origin grid. By removing the local origin points with lower confidence, the potential positive lane anchors around the groundtruth are more likely to chosen while the background lane anchors are removed. Keeping it simple, the regression branch $\phi _{reg}^{lph}\left(\cdot \right)$ and the classification branch $\phi _{cls}^{lph}\left(\cdot \right)$ consists of one conv 1x1 layers and two conv 1x1 layers correspondingly.
  
-
- 
 \begin{figure}[t]
         \centering
         \includegraphics[width=0.45\textwidth]{thsis_figure/local_polar_head.png} % 替换为你的图片文件名
@@ -284,6 +285,14 @@ Then the feature points can be sample on the line anchor. The y coordinate of po
 \end{figure}
 
 
+\begin{figure}[t]
+        \centering
+        \includegraphics[width=\linewidth]{thsis_figure/auxloss.png} % 替换为你的图片文件名
+        \caption{Auxloss for segment parameter regression.}
+        \label{auxloss}
+\end{figure}
+
+
 The RCNN Module consists of several MLP layers and predicts the confidence and the coordinate offset of $x_{i}$. During the training stage, all the $F\in \mathbb{R} ^{C_{f}\times H_{f}\times W_{f}}$ proposed anchors participate, and the SimOTA\ref{} label assignment strategy is used for the RCNN module to determine which anchors are positive anchors, irrespective of the confidence predicted by the LPM module. These strategies are employed because the negative/background anchors are also crucial for the adaptability of the RCNN module.
 
 The loss function is as follows:
@@ -806,20 +815,6 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
         \end{subfigure}
         \vspace{0.5em}
 
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/culane/1_pred.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/culane/2_pred.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/tusimple/1_pred.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/tusimple/2_pred.jpg}
-        \end{subfigure}
-        \vspace{0.5em}
-
         \begin{subfigure}{\subwidth}
                 \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/culane/1_anchor.jpg}
         \end{subfigure}
@@ -834,6 +829,20 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
         \end{subfigure}
         \vspace{0.5em}
 
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/culane/1_pred.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/culane/2_pred.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/tusimple/1_pred.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/tusimple/2_pred.jpg}
+        \end{subfigure}
+        \vspace{0.5em}
+
         \begin{tikzpicture}
                 \draw[dashed, pattern=on 8pt off 2pt, color=gray, line width=1pt] (-\textwidth/2,0) -- (\textwidth/2,0);
         \end{tikzpicture}
@@ -853,6 +862,19 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
         \end{subfigure}
         \vspace{0.5em}
         
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/llamas/1_anchor.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/llamas/2_anchor.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/dlrail/1_anchor.jpg}
+        \end{subfigure}
+        \begin{subfigure}{\subwidth}
+                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/dlrail/2_anchor.jpg}
+        \end{subfigure}
+        \vspace{0.5em}
 
         \begin{subfigure}{\subwidth}
                 \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/llamas/1_pred.jpg}
@@ -868,20 +890,6 @@ In the testing stage, anchors with the top-$k_{l}$ confidence are the chosed as
         \end{subfigure}
         \vspace{0.5em}
         
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/llamas/1_anchor.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/llamas/2_anchor.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/dlrail/1_anchor.jpg}
-        \end{subfigure}
-        \begin{subfigure}{\subwidth}
-                \includegraphics[width=\imgwidth, height=\imgheight]{thsis_figure/view_dataset/dlrail/2_anchor.jpg}
-        \end{subfigure}
-        \vspace{0.5em}
-        
         \caption{hhh}
 \end{figure*}