main.tex

@@ -134,7 +134,7 @@ To address the above two issues, we propose Polar R-CNN, a novel anchor-based me
 %
 \begin{figure*}[ht]
 	\centering
-	\includegraphics[width=0.99\linewidth]{thesis_figure/ovarall_architecture.png} 
+	\includegraphics[width=0.85\linewidth]{thesis_figure/ovarall_architecture.png} 
 	\caption{An illustration of the Polar R-CNN architecture. It has a similar pipelines with the Faster R-CNN for the task of object detection, and consists of a backbone, a FPN with three levels of feature maps, respectively denote by $P_0, P_1, P_2$, followed by a \textit{Local Polar Module}, and a RoI pooling module to extract features fed to a \textit{Global Polar Module} for lane detection. Based on the designed lane representation and lane anchor representation in polar coordinate system, the local polar module can propose sparse line anchors and the global polar module can produce the robust and accurate lane predictions. The global polar module includes a triplet head, which comprises a \textit{one-to-one (O2O)} classification head, a \textit{one-to-many} (O2M) classification head , and a \textit{one-to-many} (O2M) regression head.}
 	\label{overall_architecture}
 \end{figure*}