# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview
\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \hline
**Method** & **Year** & **Real-Time** & **Oriented** &
\begin{tabular}{c} **Undirected** \\ **Oriented** \\ \end{tabular} & \multicolumn{1}{c}{**Remarks on Method, Accuracy, and Speed**} \\ \hline YOLOv2, [39] & 2017 & \(\surd\) & & Faster and more accurate than YOLO by using anchor boxes. Its YOLOv2 \(416\times 416\) variant is faster and more accurate than SSD300 \\ \hline RetinaNet [50] & 2017 & \(\surd\) & & Matches the speed of the previous real-time methods while being more accurate than previous methods by using a focal loss function \\ \hline YOLOv3 [5] & 2018 & \(\surd\) & & On par with RetinaNet accuracy but around four times faster and more accurate, faster than YOLOv2 by incorporating multiple improvements including multiple detection at multiple scales \\ \hline
[42] & 2017 & \(\surd\) & \(\surd\) & Extends YOLOv1 by direct orientation angle regression to detect text from images \\ \hline
[43] & 2017 & \(\surd\) & \(\surd\) & Similar to YOLOv1 with adding prior anchor boxes to detect objects in satellite images \\ \hline
[44] & 2018 & \(\surd\) & \(\surd\) & Extends YOLOv2 by orientation angle regression represented as a complex number to detect objects from birds-eye-view Laser Scanner maps \\ \hline
[45] & 2020 & \(\surd\) & \(\surd\) & Solves the problem of orientation encoding discontinuous by transforming angular prediction from a regression problem to a classification task but provides coarse-granularity in orientation prediction. \\ \hline
[48; 49] & 2018, 2020 & \(\surd\) & \(\surd\) & \(\surd\) & Reference [48] extend YOLOv1 to predict quadrilaterals encoding orientation to detect text from images, and [49] extends it to predict polygons with a varying number of vertices. Both methods are not optimal for rectangle-shaped objects. \\ \hline Proposed Method & \(\surd\) & \(\surd\) & \(\surd\) & Extends YOLOv3 to predict undirected rectangle-shaped objects \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._To reduce information security risks, a validation entity such as a motorway infrastructure operator can receive and validate the vehicles' shared information. Additionally, the proposed system could aspire for \"safety-based navigation\" applications where travelers can choose not only faster and shorter routes, but also safer routes. Among the most important objects in road images in the context of safety analysis are road curbs, barriers, and lane markings. These objects appear oriented when seen from a front-facing view from a vehicle driver's perspective, and orientation is undirected (\(\theta\equiv\theta+180^{\circ}\), where \(\theta\) is the orientation angle). The majority of existing object detection methods from images, as detailed in the survey in Section 2.2, presume that detected objects appear horizontal in images. Adopting such methods while having oriented objects as in our case, produces objects bounding boxes predictions that are too loose, i.e., the bounding box is not representing the object shape nor position, big parts of the detected area cover other objects. Such downside effect is demonstrated in the results in Section 5. To overcome such limitations, a new model is developed to detect undirected oriented objects as detailed next.