# Automatic Roadway Features Detection with Oriented Object Detection
## 2 Background
### Oriented Object Detection
The method in [43] uses multi-angle prior anchor boxes to improve prediction accuracy. Such direct encoding of orientation angle leads to singularities that limit the model accuracy, which are avoided in [44] by predicting the sin and cos components of the angle encoded as a complex number (to detect objects from birds-eye-view Laser Scanner maps). The aforementioned three methods predict directed oriented objects, while this study tackles undirected oriented objects. For example, in the undirected case used in this study, it is equivalent to detect a lane marking in a front-facing camera image with an orientation of 45\({}^{\circ}\) or 225\({}^{\circ}\). However, in the directed object detectors case, detecting a car in a top-view image or map with orientation of 45\({}^{\circ}\) is not equivalent to a 225\({}^{\circ}\) of orientation. In [45], the authors tackle the problem of orientation encoding discontinuous by transforming angular prediction from a regression problem to a classification task, but the drawback is that the predicted orientation is provided in coarse-granularity. Other two-stage detectors as in [46] and [47] offer oriented object detections, but they are slower than one-stage detectors. In [48], YOLOv1 is extended to detect undirected oriented objects by predicting quadrilaterals to detect text from images, and in [49], it is extended to detect size-independent polygons with a varying number of vertices defined on a polar grid. Nevertheless, these two methods make the detection problem harder for the model to learn in case of detecting only rectangle-shaped objects, because the model prediction search space include considerably large invalid options for non-rectangular objects. In addition, the method in [48] was found to have common failure cases with vertical text objects in images. Additionally, the aforementioned five oriented object detection methods do not adopt YOLOv3 concept of multiple detection at multiple scales, which promises better detection of small objects. In this paper, the relevant detectors are investigated, and a new method is introduced to address the application in hand--as detailed in Section 3.2. \begin{table}
\begin{tabular}{c|c|c|c|c} \hline \hline
**Method** & **Year** & **Real-Time** & **Oriented** & **Undirected** & **Remarks on Method, Accuracy, and Speed** \\ \hline R-CNN [34] & 2014 & & & First stage adopts Selective Search \\  & & & & ([PERSON] et al., 2013). \\ \hline Fast R-CNN [35] & 2015 & & & Faster than R-CNN due to sharing CNN \\  & & & & computations between proposed regions. \\ \hline FasterR-CNN [36] & 2015 & & & Faster than Fast R-CNN by using Region Proposal \\  & & & & Network (RPN) \\ \hline YOLOv1 [37] & 2016 & \(\surd\) & & The first to use a single deep neural network and works in real-time at 45 FPS \\ \hline SSD [38] & 2016 & \(\surd\) & & More accurate than YOLO, does not split the image into grids, instead it predicts offsets of anchor boxes \\  & & & & for every location in a pyramidal feature maps \\  & & & & hierarchy. Its SSD300 variant is faster than YOLO. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Object detection literature.