## 3 Experimental Results
### Comparison with Other Methods  
Extracting road and detecting traffic signs along the road in one source of the input (i.e., LiDAR data or color images) is not straightforward. For instance, it is challenging to segment 3D LiDAR data and extract road from the segments mainly due to high level of noise. It is also challenging to detect traffic signs due to their low density in the airborne LiDAR data. We implemented the method proposed in [30] to segment the road in LiDAR data and the method proposed in [31] to identify traffic signs along the road in LiDAR data. To this end, we extracted 3D hand-crafted features [30], namely, normal vectors and principal curvatures, to segment road in our airborne LiDAR data. We obtained inaccurate segmentation results, which contained many of the parking lot areas along the road, due to similar normal vectors and curvatures for parking lots and road. We further extracted 3D deep features [31] to segment and classify traffic signs in our airborne LiDAR data. We obtained inaccurate segmentation and classification results since 3D deep features capture a lot of noise in the data. On the other hand, traffic signs appeared with low density in our airborne LiDAR data. As a result, they did not form any recognizable shape and were difficult to be detected as any solid objects. Our experimental results on the airborne LiDAR data showed that seven non-traffic signs were detected as traffic signs. These seven non-traffic signs could be easily filtered out if we fused the complementary information from both color images and LiDAR data. Similarly, detecting traffic signs in airborne color images is also challenging since the height and shape information of traffic signs is missing in the 2D data. We cannot compare the performance of other methods of detecting traffic signs in color images with ours since they do not process airborne color images, where traffic signs do not exhibit any rectangular shapes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & \(\mathbf{F_{1}}\)**-Measure** & **Quality** \\ \hline Traffic Sign Class & 82.35\% & 100\% & 90.32\% & 82.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of traffic sign detection results in terms of four measures. Figure 8: Extracted traffic sign candidates and their classification labels for four sections of the road. To the best of our knowledge, there is no research working on registered airborne geo-referenced color images and airborne LiDAR data to detect traffic signs. Therefore, we cannot compare the proposed method with state-of-the-art methods in this regard. In addition, comparing the performance of the proposed method with the performance of previous studies on the same dataset is challenging due to the differences in datasets and the variation of the defined tasks. For instance, one method working well on a high density MLS point cloud collected from a city area may not obtain a good performance on airborne LiDAR data collected from highway and vice versa. This is mainly due to different scan angles of objects, point cloud density, and noise level. Instead, we compared the performance of the proposed method with several traffic sign detection methods that worked with either LiDAR data or color images to demonstrate the effectiveness of the proposed fusion technique. We chose four methods [5,9,10,32], to compare their performance of detecting traffic signs in the LiDAR data with ours. However, this comparison was difficult due to the difference of datasets in terms of the quality, density, and distribution of the point clouds, areas (city vs. highway) where the data were collected from, and the data source (mobile LiDAR vs. airborne LiDAR). In [32], the authors report a recall rate of 65% and a precision rate of 58% for 60 traffic signs. A recall of 60.81% and a precision of 95.74% are reported in [5]. [PERSON] et al. [9] report a recall of 65.96% and a precision of 93.94%. [PERSON] et al.