# RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets
## 5 Experimental Results
### Detection and Classification  
In this more realistic scenario, we have developed a dataset that contains several different scenes with several objects viewed against a background containing walls as a significant feature. Thus, the scene data include examples of occluded objects, multi-path reflection and interference between objects (Figure 9). This new dataset contains the same objects (bike, trolley, cone, mannequin, sign, dog) in different parts of the room with arbitrary rotations and ranges. We also include different types of mannequin, cone, trolley, traffic sign, and bikes, meaning that those objects can have different radar signatures. This new dataset contains 198 scenes and 648 objects, with an average of 3.27 objects per scene. In summary, we have 85 examples of bikes, 189 of trolleys, 132 of mannequins, 63 of cones, 129 of signs and 50 of stuffed dogs. Some examples of scene data are shown in Figure 10.  
To apply detection and classification, we developed a two-stage methodology similar to R-CNN [23], shown in Figure 1. First, we detect potential regions of interest and then classify each detected region. The CA-CFAR (cell-averaging constant false alarm rate) detection method [24] employs an adaptive threshold based on the statistics of the region to decide whether the return is of sufficient power to be of interest. In our implementation, this was applied to the original polar image at each azimuth direction. The empirically chosen parameters used for CFAR were 500 for cell size, 30 for guard cells and 0.22 for false alarm rate. Following detection, the standard density-based spatial clustering of applications with noise (DBSCAN) [25] technique was used to form a cluster of cells and remove outliers. The parameters used for DBSCAN were also selected empirically: \(\epsilon=0.3\) m, which is the maximum distance of separation between two detected points, and \(S=40\), where \(S\) is the minimum number of points to form a cluster. We then generate fixed size bounding boxes of size \(275\times 275\)--the same size as the input from the single object images. Then, the image is resized to \(88\times 88\) as before. It is important to separate the effects of detection (by CFAR and clustering) from object classification (using the neural network); therefore, we perform two types of experiment, first, assuming perfect detection, and second, including the entire detection and classification pipeline. Thus, in the perfect detection case, the exact object location is known, and the unknown element is whether the classifier can still determine the correct object identity in the presence of the additional artefacts described above, bearing in mind that the network has been trained on the isolated objects, not the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Samples** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline Original Data & 475 & 33.36 & 39.15 & 31.39 & 39.15 \\ Original Data + SDA & 19,950 & 80.71 & 82.13 & 79.49 & 82.13 \\ \hline Original Data + SDA + SN + BS (Ours) & 19,950 & 88.51 & 83.83 & 82.42 & 83.83 \\ Original Data + SDA + RDA + BS (Ours) & 19,950 & 95.28 & 94.89 & 94.45 & 94.89 \\ Original Data + SDA + RDA + SN + BS (Ours) & 19,950 & **99.79** & **99.79** & **99.79** & **99.79** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the single-object classification task. SDA = standard (RGB) data augmentation, RDA = range data augmentation, SN = addition of speckle noise, BS = background shift. objects in the multiple object scenarios, and hence cannot learn any features that arise in that instance. As in the single object classification case, the key hypothesis is that the radar augmentation technique will give improved results when compared to the standard, optical image-derived methods. In our experiments, we classified our scenes with regard to the number of objects and at short, mid and long ranges. Extensive results are shown in Tables 6 and 7.