# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 8 Conclusions  
This work presented the results of the 2020 Drone vs. Birds detection challenge. The three best performing approaches were described and compared. The methods differ in key design aspects and the results show that complementary strengths are exhibited for each of the approaches. One should observe that the key challenges lie in the handling of moving cameras and detection of distant drones. Thus, in future editions the training and testing data will be expanded along these characteristics. A promising methodological direction for future work will be to investigate the combination of design aspects from the three approaches in order to evaluate and exploit their complementary nature. It is worth noticing that the developed algorithms do not explicitly take birds into account at the design stage in a supervised manner, since data are not annotated for birds. Consequently, in test sequences where several birds appear (in particular, in one scene an entire flock is present) false alarms generally increase for all methods, as birds are small as  
Figure 23: Images crops showing qualitative detection results and the corresponding GT (green). Note that the detection results from Team Alexis (red), Team Eagledrone (blue) and Team Gradient (yellow) are enlarged to simplify the visualization. All methods achieve good detection results for different drone types and a large range of drone sizes in different environments. Figure 24: Images crops showing qualitative detection results and the corresponding GT (green). Note that the detection results from Team Alexis (red), Team Eagledrone (blue) and Team Gradient (yellow) are enlarged to simplify the visualization. Remaining challenges are mainly small drones in front of structured backgrounds, small disturbing objects and drones in front of unseen structured backgrounds. in case of distant UAVs. Annotation of video sequences also for birds is clearly an highly time-consuming task hence is not scalable when in scenarios where many birds are found. One of the tasks to be addressed is thus how to include bird targets in the training dataset, also considering that the appearance of distant fixed-wing UAVs is very similar to birds. Further research is needed on this topic, and will be considered in the future editions of the Drone vs. Bird Detection Challenge. Another aspect that deserves investigation is the understanding and interpretation of the ultimate reasons why each algorithm has different performance on different sequences. Future editions of the challenge may evaluate additional aspects such as computational complexity (and real-time), use of additional datasets, ability to generalize to completely different backgrounds, etc., as a means to grasp understanding of the many different trade-offs at play among the numerous possible approaches and methodological combinations. Conceptualization, A.C., A.F., A.S., L.S., A.D. and D.Z. ; methodology, A.C., A.S., L.S., A.D., D.Z., M.M., D.d.I.I., I.G., J.-P.M., G.G., A.M., S.R. ; software, A.S., L.S., M.M., D.d.I.I., I.G., J.-P.M., G.G., A.M., S.R. ; validation, A.S., L.S., M.M., D.d.I.I., I.G., J.-P.M., G.G., A.M., S.R. ; writing--original draft preparation, A.C., A.F., A.D. and D.Z. ; writing-review and editing, A.C., A.F., A.S., L.S., A.D., D.Z., M.M., D.d.I.I., I.G., J.-P.M., G.G., A.M., S.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The Drone-vs-Bird challenge dataset is freely available for download upon signing a DUA. The annotations are available at [[https://github.com/woodetc/challenge](https://github.com/woodetc/challenge)]([https://github.com/woodetc/challenge](https://github.com/woodetc/challenge)), accessed on: 14 April 2021. Gradiant Team has open sourced all the code and made it available at [[https://github.com/Gradiant/pyodi](https://github.com/Gradiant/pyodi)]([https://github.com/Gradiant/pyodi](https://github.com/Gradiant/pyodi)), accessed on: 14 April 2021.