# RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets
## 5 Experimental Results
### Detection and Classification
We anticipated that, with more objects in the scene and at a greater distance, the effects of attenuation and mutual interference will cause the classification rates to fall. The metric for evaluation is average-precision (AP), which is used commonly in an object detection scenario. We use the PASCAL VOC implementation [26] with an intersection over union (IoU) equal to 0.5. AP is computed as \(AP=\int_{0}^{1}p(r)dr\), where \(p\) is precision and \(r\) is recall. We retrained the neural network from the single object dataset using both ranges (3.8 m and 6.3 m) and tested on this new more realistic scenario. All the other parameters were the same. We applied the same data augmentation methods as before. We compared the standard data augmentation (SDA) with RADIO (SDA + RDA + SN + BS). There are several key observations to be made from these experiments. First, the results in all cases are significantly poorer than in the isolated object case, even for perfect detection, as we would expect as the data are much more challenging and realistic. For example, the overall mAP for perfect detection is only 66.28%. The second conclusion is that there are significant object differences. As ever, the easy task is to recognise the trolley, with an mAP of 90.08% in the perfect case, as opposed to the mannequin at 41.15% which is often confused with the cone for example. Third, we observe that, as expected, the type of scene has a significant effect. For example, in the perfect case, the overall mAP for less than four objects is 85.13% but this drops to 52.74% for more than seven objects. Similarly, for \(4<7\) objects, the mAP drops from 79.09% to 66.58% as we move from short to long range, but the optimal scenario is often at middle range. This is probably attributable to stronger multipath effects at the shorter range, but this would require further examination. Regarding the cone object, we realised that the reflection of the cone, especially for long ranges, can get completely attenuated with values close to the background level, achieving worse results between sensed objects. Figure 9: Possible unwanted effects in the multiple object dataset. As one might expect, the results of the detection classification pipeline are worse across the board in all cases, culminating in an overall figure of 57.11% as opposed to 66.28%. However, bearing in mind the key hypothesis, the most important result is the comparison of the results with RADIO (in gray) as opposed to standard argumentation techniques. Although there is an occasional anomaly, in almost all cases, the RADIO technique is shown to be an improvement over standard augmentation techniques. Again, looking at the overall figures, the mAP improves from 51.80% to 66.28% for perfect detection, and from 42.35% to 57.11% in the pipelined case. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**AP**} & \multirow{2}{*}{**Overall**} & \multicolumn{4}{c}{**\#Objects \textless{} 4**} & \multicolumn{4}{c}{**\(\textless{}\)6 Objects \textless{} 7**} & \multicolumn{4}{c}{**\#Objects \textgreater{} 7**} & \multicolumn{4}{c}{**\#Objects \textgreater{} 7**} & \multicolumn{4}{c}{**Short**} & \multicolumn{1}{c}{**Long**} \\  & & **Overall** & **Short** & **Mid** & **Long** & **Overall** & **Short** & **Mid** & **Long** & **Overall** & **Short** & **Mid** & **Long** & **Overall** \\ \hline bike & 65.78 & 81.7 & 50.0 & 88.89 & 75.0 & 53.82 & 0.0 & 59.92 & 66.67 & 66.36 & N/A & 67.2 & N/A & 25.0 & 69.31 & 71.43