## 3 Methodology  
In this section, we present the proposed model structure and experimental execution process in detail, and we elaborate on the method adopted in the core part of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Authors and** \\ **Documents** \\ \end{tabular} & **Year** & **Method** & **Features** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Traditional \\ Algorithm \\ \end{tabular} } & Se [37] & 2000 & \begin{tabular}{c} Crosswalk detection algorithm based on equidistant straight lines \\ and vanishing point constraints \\ \end{tabular} & Segment edges \\ \cline{2-4}  & [PERSON] et al. [38] & 2005 & \begin{tabular}{c} Crosswalk detection based on the bipolar feature method \\ \end{tabular} & Bipolar feature method \\ \cline{2-4}  & [PERSON] et al. [39] & 2009 & Arrow marking recognition algorithm based on multi-class SVM & SVM \\ \cline{2-4}  & [PERSON] et al. [40] & 2011 & Real-time detection of arrow markings using curve-based prototype & Curve-based prototype \\  & & fitting & fitting \\ \cline{2-4}  & [PERSON] et al. [41] & 2011 & Detection and recognition of urban road markings using images & Image recognition \\ \cline{2-4}  & [PERSON] et al. [42] & 2014 & A road marking extraction algorithm using mobile laser scanning (MLS) data & Mobile laser scanning \\ \cline{2-4}  & [PERSON] et al. [43] & 2015 & The use of mobile LiDAR data for rapidly updating road markings & \begin{tabular}{c} Mobile LiDAR data \\ analysis \\ \end{tabular} \\ \cline{2-4}  & [PERSON] et al. [44] & 2015 & Crosswalk detection based on MSER and ERANSAC & MSER and ERANSAC \\ \cline{2-4}  & [PERSON] et al. [45] & 2019 & Block-based Hough transform for recognition of zebra crossing in natural scene images & Hough transform \\ \cline{2-4}  & [PERSON] et al. [46] & 2022 & A crosswalk stripe detection model based on gradient similarity tags & Gradient similarity tags \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [47] \\ \end{tabular} } & [PERSON] et al. [47] & 2014 & Detecting and identifying crosswalks based on RGB-D images & SVM \\ \cline{2-4}  & [PERSON] et al. [48] & 2016 & Recognition of damaged arrow–road markings by visible light & CNN \\ \cline{2-4}  & [PERSON] et al. [49] & 2017 & Crosswalk and guide arrow marking using neural networks based on MLS data & Image feature vector \\ \cline{2-4}  & [PERSON] et al. [50] & 2019 & MASK R-CNN for pedestrian crosswalk detection and instance segmentation & MASK R-CNN \\ \cline{2-4}  & [PERSON] et al. [51] & 2019 & Deep RetinaNet-based detection and classification of road markings & RetinaNet \\ \cline{2-4}  & [PERSON] et al. [52] & 2019 & \begin{tabular}{c} Guidance arrow detection and recognition based on adaptive \\ regions of interest and deep learning \\ \end{tabular} & Regions of interest \\ \cline{2-4}  & [PERSON] et al. [53] & 2020 & \begin{tabular}{c} The information is obtained from the classification of images \\ belonging to the separations, intersections, and crosswalks on the \\ road using a new model and VggNet, AlexNet, LeNet \\ \end{tabular} & CNN \\ \cline{2-4}  & [PERSON] et al. [54] & 2021 & Lightweight crosswalk semantic seg. network & Semantic Seg. \\ \cline{2-4}  & [PERSON] et al. [55] & 2022 & \
\begin{tabular}{c} CDNet: a real-time and robust crosswalk detection network on \\ Jetson Nano based on YOLOv5 \\ \end{tabular} & CDNet \\ \cline{2-4}  & [PERSON] et al. [56] & 2023 & Deformable polar polygon object detection & DPPD \\ \cline{2-4}  & [PERSON] et al. [57] & 2023 & Edge AI-based scene-specific object detection & YOLOv5 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. [58] \\ \end{tabular} } & [PERSON] et al. [58] & 2024 & Fall detection with YOLOPose, PCA, CNN, LSTM & YOLOPose \\ \cline{2-4}  & [PERSON] et al. [59] & 2024 & Multi-channel CoordConv module detection & CFG\_SI\_YOLO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of research on crosswalk and guide arrow detection.