# Automatic Roadway Features Detection with Oriented Object Detection
## 5 Analysis and Results
### Oriented Objects Detection  
Table 3 reports mAP-50 percentage results of the four different models for object detection detailed in Section 3.2:  
* Model 1 is the original YOLOv3 [5] model trained on our dataset without the angle orientation. * Model 2 adopted the direct orientation encoding. * Model 3 encodes the orientation angle \(\theta\) as \(\sin(\theta)\) and \(\cos(\theta)\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Roadway Functional** & **Kilometers** & **Number of** & **Percentage in** \\
**Classification** & **Travelled** & **Snapshots** & **the Dataset** \\ \hline Primary Highways & 176 & 1760 & 37.19\% \\ Regional Primary Highways & 164.1 & 1641 & 34.68\% \\ Primary Arterial Roads & 74.2 & 742 & 15.68\% \\ Secondary Arterial Roads & 21.5 & 215 & 4.54\% \\ Primary Collector Roads & 29 & 290 & 6.13\% \\ Local Roads & 8.4 & 84 & 1.78\% \\ Total & 473.2 & 4732 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distances covered and number of snapshots for each road functional classification. Figure 5: Data collection design in the Greater Cairo Region (GCR). * Model 4 is the developed model adopting sin(20) and cos(20) encoding of the orientation angle; while not using data augmentation during training. * Model 5 is the developed model adopting sin(20) and cos(20) encoding of the orientation angle. Data augmentation is found to increase the mAP; model 5 achieves 7.2% higher mAP than model 4. Comparing the best performing model (model 5) with the original YOLOv3 model (model 1) shows an improvement of 16.9% mAP in detecting undirected oriented objects. On GCR test dataset, the introduced model achieves a 50.3% mAP. In our view, this is a significant achievement as the YOLOv3 is trained on MS COCO Common Objects in Context dataset which is more than 40 times larger than the current research dataset and achieves a 57.9% mAP, not to mention the added complexity and challenges of detecting oriented objects compared to the horizontal objects in COCO dataset. As reported in Table 3, the'sin(0) and cos(0)' model achieves the best overall mAP result. However, by inspecting the individual classes average precision (AP) as shown in Table 4, the'sin(20) and cos(20)' model (model 5) is demonstrated to perform best for all the oriented classes like lane markings and curbs. Objectively, model 5 achieves more accurate orientation predictions than model 3, as shown in the random test data samples results in Table 5, as in sample numbers 3, 5, 6, 8, and 9. Accurate orientation prediction reduces the amount of redundant boxes corresponding to the same actual object, as duplicates have slight 0 differences, and hence produce large IoUs that makes them eliminated as duplicates during non-maximum suppression. This demonstrates the effectiveness of the introduced'sin(20) and cos(20)' model. On the other hand, the other non-oriented classes predictions did not benefit from such method. Table 4 also reports the object class distribution in the dataset. Marking, light pole, building, billboard, car, tree, and public transportation classes are highly represented, while traffic sign, right barrier, left barrier, right curb, left curb, pedestrian, and heavy vehicle classes appear less, and fence, bus, and motorcycle are the least represented classes in the dataset.