###### Abstract
From the perspective of information and communication technology, deep-learning-based object detection [2], utilizing deep convolutional neural networks (CNNs) as the backbone and convolution as the basis of calculation, offers a feasible and effective solution for the problems outlined above. Specifically, deep-learning-based object detection technologies can be used alongside image-preprocessing techniques to train object detection systems. Sensor components can be used to observe and recognize speed limit signs, rockfalls, potholes, and car crashes (involving damaged cars and overturned cars) on roads. High-performance computers could then effectively calculate the information to generate real-time reminders to drivers to pay attention to the road conditions ahead, thus preventing violations, car crashes, or secondary car crashes. However, to date, the concurrent use of the two typical types of models in the deep-learning-based object detection field, namely, You Only Look Once (YOLO) series models (e.g., YOLO [3], YOLO9000 [4], YOLOv3 [5], YOLOv4 [6], YOLOv5 [7], YOLOv6 [8], YOLOv7 [9]) and region-based CNN (R-CNN) series models (e.g., R-CNN [10], Fast R-CNN [11], Faster R-CNN [12], Mask R-CNN [13]) with image-processing techniques for road safety detection applications still has room for research. Moreover, there is a paucity of research available on the concurrent detection of speed limit signs, rockfalls, potholes, \begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Total** \\ \hline
2016 & 305,556 \\
2017 & 296,826 \\
2018 & 320,315 \\
2019 & 341,972 \\
2020 & 362,393 \\
2021 & 358,221 \\
2022 & 375,632 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of road traffic accidents in Taiwan over the past seven years [1]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Total** \\ \hline
2016 & 2,783,751 \\
2017 & 2,816,540 \\
2018 & 2,953,940 \\
2019 & 3,130,010 \\
2020 & 3,169,631 \\
2021 & 3,572,665 \\
2022 & 3,621,383 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of speeding violations in Taiwan over the past seven years [1]. and car crashes as well as on resolving the speed limits of different vehicles. Compared to the R-CNN series models, the YOLO series models have undergone rapid developments. Interestingly, in January 2023, Ultralytics released the all-new YOLOv8 [14], which is an integration of its seven predecessors and features several new functions such as segmentation, pose estimation, and tracking. In May 2023, Deci further optimized YOLOv8 and released the YOLO-neural architecture search (NAS) model [15], which outperforms the eight previous YOLO models. However, YOLOv8 and YOLO-NAS are more suitable for solving problems in larger and more complex settings, which explains why they have higher computing costs and complexities than the seven previous YOLO models. In other words, the excellence of YOLOv8 and YOLO-NAS are more pronounced when solving problems in larger and more complex settings. These models are unnecessary in this study due to the smaller scale of the problem settings and the limited hardware resources. It should be noted that these models are all two-dimensional (2D) object detection models that are trained using images. A substantial body of research has been conducted in recent years on the detection of three-dimensional (3D) objects using light detection and ranging (LiDAR) point clouds [16; 17]. Due to the fact that LiDAR point clouds essentially do not vary with lighting, and LiDAR-based detection is extremely precise, these models are widely applied to self-driving cars [18]. However, compared to 2D object detection, 3D object detection has higher computing costs and longer delays, which are barriers to the low delay requirements in self-driving cars. Furthermore, compared to 2D visual sensors (e.g., video cameras and dashcams), LiDAR is more expensive and is incompatible with most car models.