# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 7 Performance Comparison
The fact that also AP is comparatively good indicates that many of these false positive detections \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sequence** & **Static** & **Alexis [\%]** & **Eagledrone [\%]** & **Gradient [\%]** \\ \hline GOP8543\_004 & ✓ & 94.3 & 74.1 & **97.7** \\ GOP8545\_002 & ✓ & 98.1 & 94.7 & **98.7** \\ GOP8547\_001 & ✓ & **69.6** & 62.2 & 65.7 \\ dji_matrice\_210\_midrange\_cruise & ✓ & 99.8 & 97.1 & **99.9** \\ dji\_mavick\_mountain\_cross & ✗ & **87.2** & 80.2 & 77.3 \\ dji\_phantom\_mountain & ✓ & **78.2** & 52.3 & 63.1 \\ dji\_phantom\_close & ✓ & 99.3 & **99.4** & 98.8 \\ two\_parrot\_2 & ✓ & 93.7 & **95.0** & 93.5 \\ parrot\_disco long\_session\_2\_1m & ✗ & 28.9 & 37.3 & **42.3** \\
2019\_08\_19\_C0001\_27\_46\_solo\_1m & ✓ & 88.6 & 51.1 & **90.0** \\
2019\_08\_19\_C0001\_57\_00\_inspire\_1m & ✓ & 87.9 & 81.0 & **91.2** \\
2019\_09\_02\_C0002\_11\_18\_solo & ✓ & 89.1 & 92.2 & **94.6** \\
2019\_10\_16\_C0003\_52\_30\_mavic & ✗ & 15.8 & 36.3 & **54.8** \\
2019\_11\_14\_C0001\_11\_23\_inspire\_1m & ✓ & 49.3 & **59.9** & 38.9 \\ Overall & & 79.8 & 66.8 & **80.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed comparison best run for each team in the Drone vs. Bird Detection Challenge 2020. The first column denotes static or moving camera. The evaluation metric used for this dataset is AP. The evaluation is given for every sequence of the test set. The overall averaged result is given at the bottom. are of a very low confidence score and thus do not affect AP very strongly and could be filtered out in practical application. Figures 23 and 24 show qualitative detection results of each teams' best submission. All methods achieve good detection results for different drone types and a large range of drone \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**\#GT**} & \multicolumn{2}{c}{**Alexis**} & \multicolumn{2}{c}{**Eagledrone**} & \multicolumn{2}{c}{**Gradiant**} \\  & & **\#Det** & **Recall** & **\#Det** & **Recall** & **\#Det** & **Recall** \\ \hline GOPR5843\_004 & 438 & 1381 & 97.5 & 368 & 77.2 & 447 & 98.2 \\ GOPR5845\_002 & 544 & 1330 & 99.4 & 577 & 96.7 & 836 & 99.8 \\ GOPR5847\_001 & 318 & 443 & 70.4 & 217 & 62.3 & 427 & 66.0 \\ dji\_matrice\_210\_midrange\_cruise & 1469 & 1853 & 99.9 & 1498 & 98.5 & 1499 & 99.9 \\ dji\_mavick\_mountain\_cross & 1551 & 3528 & 91.1 & 1453 & 83.0 & 2223 & 81.8 \\ dji\_phantom\_mountain & 1663 & 2668 & 84.0 & 1245 & 56.2 & 1774 & 69.5 \\ dji\_phantom\_close & 1193 & 1307 & 99.6 & 1195 & 99.6 & 1188 & 98.9 \\ two\_parrot\_2 & 2789 & 2951 & 96.0 & 2990 & 96.2 & 2783 & 95.1 \\ parrot\_disc\_long\_session\_2\_1m & 789 & 2585 & 43.3 & 1479 & 55.1 & 2151 & 68.7 \\
2019\_08\_19\_C0001\_27\_46\_solo\_1m & 926 & 10,625 & 92.9 & 727 & 59.7 & 10,394 & 92.9 \\
2019\_08\_19\_C0001\_57\_00\_inspire\_1m & 1219 & 8178 & 90.4 & 1052 & 81.9 & 6338 & 93.1 \\
2019\_09\_02\_C0002\_11\_18\_solo & 626 & 8152 & 89.1 & 676 & 94.4 & 4284 & 95.4 \\
2019\_10\_16\_C0003\_52\_30\_mavic & 733 & 57,005 & 40.2 & 478 & 44.1 & 1188 & 68.8 \\
2019\_11\_14\_C0001\_11\_23\_inspire\_1m & 368 & 16,928 & 68.2 & 1156 & 64.9 & 21,898 & 56.8 \\ Overall & 14,626 & 118,934 & 87.4 & 15,102 & 72.9 & 57,430 & 87.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of detection results in terms of number of submitted detections (#Det) and resulting recall. Figure 22: Comparison of the detection results in terms of precision-recall curves. sizes in different environments (see Figure 23). Negative detection results given in Figure 24 indicate remaining challenges such as small drones in front of structured backgrounds, small disturbing objects and drones in front of unseen structured backgrounds.