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REVIEW 2 major objections 5 minor 78 references

Drone Detection with Event Cameras

T0 review · 2 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This survey argues that event cameras, by reporting asynchronous brightness changes at microsecond resolution, give counter-UAV systems a sensing foundation frame-based cameras lack.

desk verdict A useful, well-organized survey of event-based drone detection, but its abstract oversells robustness in extreme lighting in a way the paper's own Section 3 contradicts. read the letter →

arxiv 2508.04564 v1 pith:SJ3G3LUD submitted 2025-08-06 cs.CV

Gabriele Magrini , Lorenzo Berlincioni , Luca Cultrera , Federico Becattini , Pietro Pala This is my paper · ORCID
classification cs.CV
keywords eventcamerasneuromorphicvisiondronedetectioncounter-UAVsystemsspikingneuralnetworksrepresentationstrackingpropellersignatureanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is a survey that argues event-based vision is the right sensor foundation for counter-UAV systems because event cameras report per-pixel brightness changes asynchronously at microsecond timescales instead of capturing fixed-rate frames. That single mechanism yields three properties that matter for drone detection: no motion blur for fast targets, a dynamic range above 120 dB that survives backlight and low light, and sparse output that naturally cancels static background. The survey organizes the field by how the event stream is represented—accumulated frames, point clouds, voxel grids, time-retaining frames, or raw spikes for spiking networks—and extends the argument beyond detection to tracking, trajectory forecasting, and identification via propeller blade signatures. A sympathetic reading is that the evidence across datasets and tasks supports the conclusion that event-based vision is a reliable, low-latency, efficient basis for next-generation counter-UAV systems.

What carries the argument

The central object is the event stream $E = \{e_i = (x_i, y_i, p_i, t_i)\}$, where each event records pixel coordinates, polarity of the log-brightness change, and a microsecond-resolution timestamp. The argument is carried by the choice of how this asynchronous stream is turned into something a network can consume: accumulated event frames (two polarity channels), 3D voxel grids with time bins, time-retaining frames such as Time Surfaces, event point clouds, or raw spikes for spiking neural networks. Each representation trades retained temporal detail against compatibility with conventional architectures; the survey uses this taxonomy to explain both why early systems work and why end-to-en

What would settle it

Run a paired field test under the exact failure conditions named in the survey: a small quadrotor flying against a bright sky, in rain, and at decreasing pixel sizes, recorded simultaneously by a frame camera and an event camera. Measure detection rate and latency at each condition. If event-based detection does not beat frame-based detection in the blur or high-contrast regimes, or collapses for very small or distant targets, the survey's central recommendation loses its empirical base.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that event cameras remove the two failure modes that make frame-based cameras inadequate for drone detection—motion blur from fast angular motion and loss of contrast in extreme lighting—and at the same time provide a built-in attentional mechanism, because static scene elements produce no events. The survey asserts that this combination makes event-based vision a viable foundation for reliable, low-latency counter-UAV systems, and it supports that assertion by tracing the full pipeline: event generation, event representation, detection architectures, benchmarks, and downstream tasks. A distinctive sub-claim is that a drone's propeller, which is

Load-bearing premise

The argument depends on event cameras actually delivering their claimed microsecond temporal resolution, over-120 dB dynamic range, blur-free output, and sparse background suppression in the real conditions where drones must be detected—not just in the controlled or simulated settings cited.

Editorial extensions

If this is right

  • Detection systems can be built around event cameras for scenarios where frame-based cameras saturate or blur, including drones against bright sky, in low light, and in fast close-range maneuvers.
  • A single event stream can support the full counter-UAV chain—detection, tracking, trajectory forecasting, and identification—so downstream tasks need not require a separate sensor modality.
  • Propeller signature analysis gives an uncooperative way to identify drones and estimate rotor speed and attitude, enabling applications like virtual fences, autonomous following, and mid-air landing.
  • Event-plus-RGB multimodal systems can cover the event camera's blind spot for stationary objects while retaining event-based temporal precision.
  • Low-power neuromorphic processors running spiking networks can move drone detection to the edge with latency and energy budgets that frame-based deep learning cannot match.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the same event stream that supports detection could be fused with radar or RF feeds to cover the sensor's known blind spot for stationary drones, since static objects generate no events; a multimodal sensor suite is a natural completion of the counter-UAV picture.
  • Beyond the paper: the survey's evidence points to a measurable crossover point—at some combination of target angular velocity and apparent size, frame-based detectors degrade while event-based detectors hold; identifying that crossover experimentally would turn the survey's qualitative argument into a design rule.
  • Beyond the paper: propeller-signature methods reported here imply event cameras could perform long-range, non-cooperative drone identification; the open question is the maximum distance at which propeller events remain resolvable, which would determine whether the method is limited to close-range or scales to surveillance.
  • Beyond the paper: because the survey reports event-based models outperforming RGB ones on forecast benchmarks, the next test is ablating the event representation itself—frames versus voxel grids versus point clouds versus spiking input—on the same forecasting task to find which encoding carries the advantage.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This paper surveys event-based vision for drone detection and related counter-UAV tasks. It reviews sensor principles, event representations (event frames, point clouds, voxel grids, time-retaining frames, and end-to-end spiking pipelines), methods for detection, tracking, forecasting, and propeller-blade analysis, and it catalogs public and synthetic datasets in Table 1. The central claim is that event cameras' microsecond temporal resolution, >120 dB dynamic range, and sparse output make them a powerful foundation for reliable, low-latency, and efficient counter-UAV systems. The paper is primarily qualitative; it presents no new experiments or quantitative comparisons, but it offers a useful taxonomy and an organized overview of the literature.

Significance. If its claims are accepted, the survey consolidates a young and fast-moving field and could guide system-level design choices. Its categorization of event representations and tasks, the dataset comparison in Table 1, and the discussion of propeller-signature analysis are useful and broadly accurate. The survey also contains relevant caveats, such as the admission that low-light and rain/snow are challenging for event cameras (§3) and that frame-based accumulation can blur fast motion (§4). However, these caveats stand in tension with the unqualified claims in the Abstract and Introduction. Because the paper's headline advantages are asserted rather than demonstrated, and because the body itself weakens those assertions, the survey currently overstates its case. A revised version that separates sensor-level capabilities from system-level evidence and that verifies or softens exclusivity claims would significantly improve the paper.

major comments (2)
  1. [Abstract; §1; §3 (Challenges of Drone Detection)] The Abstract states that event cameras 'enable consistent detection in extreme lighting,' and §1 says the >120 dB dynamic range yields 'robust performance in extreme lighting conditions.' In direct tension, §3 lists 'low light environments, and harsh meteorological conditions (eg. rain, snow) which are challenging for event cameras too [9,38]'. Low light is a lighting condition, and rain/snow are precisely the extreme scenarios that a counter-UAV system must handle. Since this caveat concerns the exact capability used to motivate the survey, the headline claim should be narrowed (e.g., to high-contrast/backlit scenes) or supported by stratified results from datasets such as FRED [38], which reportedly contains rain and low-light splits.
  2. [Abstract; §4 (Frame-Based Representations)] The Abstract's 'virtually eliminate motion blur' is a sensor-level statement, but many of the surveyed detectors convert events into fixed-window accumulated frames (Mandula et al. [39], Magrini et al. [35], Zundel et al. [78], etc.). §4 itself notes that frame-based aggregation 'can blur fast motion and miss brief dynamics critical for detecting small, fast drones.' Thus, the systems actually recommended in the survey may not deliver the advertised blur-free operation. Please qualify the claim—native event processing avoids exposure-time blur, but temporal integration into frames reintroduces it—or state which of the reviewed methods preserve true asynchronous processing.
minor comments (5)
  1. [Fig. 1 caption] Typo: 'trough' should be 'through' in 'the number of papers trough the years.'
  2. [§5 (Drone Forecasting)] The sentence 'Liang et al. Liang et al. [33]' repeats the authors' names. Also, the claim that Magrini et al. [38] is 'the only publicly available benchmark for event-based drone forecasting' is unsupported by a systematic literature check; since [38] is by the same authors, the claim should be softened to 'to our knowledge' or independently verified.
  3. [§4 and Table 1] Inconsistent formatting: 'Detr' should be 'DETR'; 'F-UA V-D' and 'Anti-UA V' contain awkward hyphenation/spacing ('UAV' is standard). A style pass would improve readability.
  4. [Table 1] The header 'RGB / Event' is ambiguous. Adding a short legend (e.g., checkmark means modality is available; otherwise absent) would make the table self-explanatory.
  5. [§3 (formal definitions)] The polarity is defined as pi ∈ {0,1}. Many event-camera papers use {−1,+1}; a brief comment on the convention would avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a survey whose claims rest on external sensor properties and independent benchmarks; the internal extreme-lighting caveat is an overclaim, not a circular reduction.

full rationale

This paper is a survey and taxonomy, not a derivation or prediction pipeline. It does not fit a parameter and then relabel it as a prediction, and no equation in the paper reduces a claimed result to its own inputs. The central claims about event cameras—microsecond temporal resolution, >120 dB dynamic range, sparse asynchronous output—are supported by citations to external sensor literature and by multiple independent datasets and systems (e.g., EventVOT, EVPropNet, the Gallego et al. survey). The authors do cite their own prior work (FRED, NeRDD, Ev-Flying, Spike-TBR), but these are presented as items in the surveyed literature and as publicly available benchmarks, not as an unverified uniqueness theorem that forces the paper's conclusions. The one tension worth noting is that the Abstract and Section 1 promise "consistent detection in extreme lighting" while Section 3 explicitly lists "low light environments, and harsh meteorological conditions (eg. rain, snow) which are challenging for event cameras too [9, 38]." This is a limitation/overclaim about the strength of the evidence, not a circular argument: the survey's recommendation would be weakened by the caveat, but the recommendation is not logically identical to any fitted input or self-citation. Therefore no significant circularity is present.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No new parameters or entities are introduced. The survey relies on accepted event camera properties and the corpus of cited papers.

assumptions (2)
  • domain assumption Event cameras have microsecond temporal resolution, >120 dB dynamic range, and low power consumption.
    Stated as fact in Sec. 1; the survey's case for event cameras depends on these properties being accurate.
  • domain assumption The cited works' reported results and benchmark numbers are correct and representative.
    The survey aggregates claims from external papers (e.g., EV-Tach 0.03% error, EVPropNet transfer) without independent verification.

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Cite this review

Pith. "Pith review of Drone Detection with Event Cameras." pith.science (2026). https://pith.science/paper/SJ3G3LUD

@misc{pith2026250804564,
  author       = {Pith},
  title        = {Pith review of: Drone Detection with Event Cameras},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SJ3G3LUD}},
  note         = {Machine review of arXiv:2508.04564}
}
read the original abstract

The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably detect these targets due to their small size, high agility, and the resulting motion blur and poor performance in challenging lighting conditions. This paper surveys the emerging field of event-based vision as a robust solution to these problems. Event cameras virtually eliminate motion blur and enable consistent detection in extreme lighting. Their sparse, asynchronous output suppresses static backgrounds, enabling low-latency focus on motion cues. We review the state-of-the-art in event-based drone detection, from data representation methods to advanced processing pipelines using spiking neural networks. The discussion extends beyond simple detection to cover more sophisticated tasks such as real-time tracking, trajectory forecasting, and unique identification through propeller signature analysis. By examining current methodologies, available datasets, and the distinct advantages of the technology, this work demonstrates that event-based vision provides a powerful foundation for the next generation of reliable, low-latency, and efficient counter-UAV systems.

Figures

Figures reproduced from arXiv: 2508.04564 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Drone Sensing Taxonomy. We group the related works taken into account and extrapolate a precise taxonomy. due to growing security concerns, including risks related to unauthorized surveillance, airspace violations, and breaches of restricted zones. Significant effort has been dedicated to improving the detection of unidentified flying objects across multiple sen￾sor domains. The most common approach relies on the RG… view at source ↗
Figure 3
Figure 3. Event data representations. Comparison of different views of the event data for a drone. Samples from the FRED dataset [38]. These studies demonstrate that frame-based event repre￾sentations can be effectively integrated into conventional vi￾sion pipelines. They enable low-latency inference, efficient implementation on existing hardware, and compatibility with widely used neural architectures such as ResNet [23], Mo… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Drone tracking task. Drone tracking annotations taken from the FRED dataset [38]. Different colors correspond to differ￾ent drones. learning an edge-aware similarity metric more aligned with the information content of events. More recently, spik￾ing neural networks hav…
Figure 5
Figure 5. Figure 5: Drone forecasting task. Annotation for the forecasting task from FRED [38]. Blue: past; Green: GT; Red: prediction. ever, the benefits of event cameras have been demonstrated in the literature [43]. Despite this, only a few works ex￾ist on forecasting the trajectories …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

78 extracted references · 67 canonical work pages

  1. [38]

    Fred: The florence rgb-event drone dataset

    Gabriele Magrini, Niccol `o Marini, Federico Becattini, Lorenzo Berlincioni, Niccol `o Biondi, Pietro Pala, and Al- berto Del Bimbo. Fred: The florence rgb-event drone dataset. In Proceedings of the 33rd ACM International conference on multimedia, 2025. 2, 3, 4, 5, 6, 7, 8

    work page 2025

  2. [39]

    Towards real-time fast unmanned aerial vehicle de- tection using dynamic vision sensors

    Jakub Mandula, Jonas K ¨uhne, Luca Pascarella, and Michele Magno. Towards real-time fast unmanned aerial vehicle de- tection using dynamic vision sensors. In Proceedings of the 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pages 1–6, 2024. 3, 7, 8

    work page 2024

  3. [35]

    Neuromorphic drone detec- tion: an event-rgb multimodal approach

    Gabriele Magrini, Federico Becattini, Pietro Pala, Alberto Del Bimbo, and Antonio Porta. Neuromorphic drone detec- tion: an event-rgb multimodal approach. In European Con- ference on Computer Vision, pages 259–275. Springer, 2024. 2, 3, 5, 7, 8

    work page 2024

  4. [78]

    Bimodal vision system combining standard camera and dy- namic vision sensor for detecting and tracking fast uavs

    Antoine Zundel, C ´edric Demonceaux, Nicolas Hueber, Damien Spittler, Guillaume Strub, and S ´ebastien Changey. Bimodal vision system combining standard camera and dy- namic vision sensor for detecting and tracking fast uavs. In Emerging Imaging and Sensing Technologies for Security and Defence IX, page 1320402. SPIE, 2024. 3

    work page 2024

  5. [1]

    Night-time detection of uavs using thermal in- frared camera

    Petar Andra ˇsi, Tomislav Radiˇsi´c, Mario Mu ˇstra, and Jurica Ivoˇsevi´c. Night-time detection of uavs using thermal in- frared camera. Transportation research procedia, 28:183– 190, 2017. 2

    work page 2017

  6. [2]

    Mul- tiple simultaneous rotation event-based angular speed mea- surement

    George O de A Azevedo, Leandro H de S Silva, Agostinho Freire, Rog´erio P De Ara´ujo, and Bruno JT Fernandes. Mul- tiple simultaneous rotation event-based angular speed mea- surement. IEEE Sensors Journal, 2025. 6

    work page 2025

  7. [3]

    Monocular event-based vision for obstacle avoidance with a quadrotor

    Anish Bhattacharya, Marco Cannici, Nishanth Rao, Yuezhan Tao, Vijay Kumar, Nikolai Matni, and Davide Scaramuzza. Monocular event-based vision for obstacle avoidance with a quadrotor. In Conference on Robot Learning , pages 4826–

    work page

  8. [4]

    Counter un- manned aerial systems testing: Evaluation of vis swir mwir and lwir passive imagers

    Gabriel Carlisle Birch and Bryana Lynn Woo. Counter un- manned aerial systems testing: Evaluation of vis swir mwir and lwir passive imagers. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017. 2

    work page 2017

Show all 78 references
  1. [5]

    Eventboost: Event-based acceleration platform for real-time drone localization and tracking

    Hao Cao, Jingao Xu, Danyang Li, Zheng Yang, and Yun- hao Liu. Eventboost: Event-based acceleration platform for real-time drone localization and tracking. In IEEE IN- FOCOM 2024-IEEE Conference on Computer Communica- tions, pages 1851–1859. IEEE, 2024. 2

    2024

  2. [6]

    End-to- end object detection with transformers

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to- end object detection with transformers. In European confer- ence on computer vision, pages 213–229. Springer, 2020. 3

    2020

  3. [7]

    Drone detection using depth maps

    Adrian Carrio, Sai Vemprala, Andres Ripoll, Srikanth Sari- palli, and Pascual Campoy. Drone detection using depth maps. In 2018 IEEE/RSJ international conference on intel- ligent robots and systems (IROS) , pages 1034–1037. IEEE,

    2018

  4. [8]

    Siamevent: Event-based object tracking via edge-aware similar- ity learning with siamese networks

    Yujeong Chae, Lin Wang, and Kuk-Jin Yoon. Siamevent: Event-based object tracking via edge-aware similar- ity learning with siamese networks. arXiv preprint arXiv:2109.13456, 2021. 5

    2021 arXiv

  5. [9]

    Event-based tiny object detection: A bench- mark dataset and baseline

    Nuo Chen, Chao Xiao, Yimian Dai, Shiman He, Miao Li, and Wei An. Event-based tiny object detection: A bench- mark dataset and baseline. arXiv preprint arXiv:2506.23575,

    arXiv

  6. [10]

    Optical and acoustical uav detection

    Frank Christnacher, S ´ebastien Hengy, Martin Laurenzis, Alexis Matwyschuk, Pierre Naz, St ´ephane Schertzer, and Gwenael Schmitt. Optical and acoustical uav detection. In Electro-Optical Remote Sensing X, pages 83–95. SPIE, 2016. 2

    2016

  7. [11]

    Drone vs

    Angelo Coluccia, Alessio Fascista, Arne Schumann, Lars Sommer, Anastasios Dimou, Dimitrios Zarpalas, Miguel M´endez, David De la Iglesia, Iago Gonz ´alez, Jean-Philippe Mercier, et al. Drone vs. bird detection: Deep learning al- gorithms and results from a grand challenge. Sen...

    2021

  8. [12]

    A new stereo fisheye event camera for fast drone detection and tracking

    Daniel Rodrigues Da Costa, Maxime Robic, Pascal Vasseur, and Fabio Morbidi. A new stereo fisheye event camera for fast drone detection and tracking. In IEEE International Conference on Robotics and Automation, 2025. 7

    2025

  9. [13]

    Drift: Autonomous drone dataset with integrated real and synthetic data, flexible views, and transformed domains

    Fardad Dadboud, Hamid Azad, Varun Mehta, Miodrag Bolic, and Iraj Mantegh. Drift: Autonomous drone dataset with integrated real and synthetic data, flexible views, and transformed domains. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages 6900–

    2025

  10. [14]

    Loihi: A neuromorphic manycore processor with on-chip learning

    Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Di- mou, Prasad Joshi, Nabil Imam, Shweta Jain, et al. Loihi: A neuromorphic manycore processor with on-chip learning. Ieee Micro, 38(1):82–99, 2018. 5

    2018

  11. [15]

    Flight-test evaluation of small form-factor lidar and radar sensors for suas detect-and-avoid applications

    Maarten Uijt De Haag, Chris G Bartone, and Michael S Braasch. Flight-test evaluation of small form-factor lidar and radar sensors for suas detect-and-avoid applications. In 2016 IEEE/AIAA 35th digital avionics systems conference (DASC), pages 1–11. IEEE, 2016. 2

    2016

  12. [16]

    35 ghz fmcw drone detection system

    Jedrzej Drozdowicz, Maciej Wielgo, Piotr Samczynski, Krzysztof Kulpa, Jaroslaw Krzonkalla, Maj Mordzonek, Marcin Bryl, and Zbigniew Jakielaszek. 35 ghz fmcw drone detection system. In 2016 17th International Radar Sympo- sium (IRS), pages 1–4. IEEE, 2016. 2, 6

    2016

  13. [17]

    Drone detection us- ing a low-power neuromorphic virtual tripwire

    Anton Eldeborg Lundin, Rasmus Winzell, Hanna Hamrell, David Gustafsson, and Hannes Ovr ´en. Drone detection us- ing a low-power neuromorphic virtual tripwire. In European Conference on Computer Vision , pages 105–120. Springer,

  14. [18]

    A density-based algorithm for discovering clusters in large spatial databases with noise

    Martin Ester, Hans-Peter Kriegel, J ¨org Sander, Xiaowei Xu, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, pages 226–231,

  15. [19]

    Event-based vision: A survey

    Guillermo Gallego, Tobi Delbr ¨uck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, J ¨org Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1)...

    2020

  16. [20]

    Implementation of detec- tion and tracking mechanism for small uas

    Sai Ram Ganti and Yoohwan Kim. Implementation of detec- tion and tracking mechanism for small uas. In 2016 Interna- tional Conference on Unmanned Aircraft Systems (ICUAS) , pages 1254–1260. IEEE, 2016. 2

    2016

  17. [21]

    Video to events: Recycling video datasets for event cameras

    Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carri ´o, and Davide Scaramuzza. Video to events: Recycling video datasets for event cameras. In IEEE Conf. Comput. Vis. Pat- tern Recog. (CVPR), 2020. 8

    2020

  18. [22]

    Fast r-cnn

    Ross Girshick. Fast r-cnn. In Proceedings of the IEEE inter- national conference on computer vision , pages 1440–1448,

  19. [23]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 4

    2016

  20. [24]

    Hoseini, G

    S. Hoseini, G. Orchard, A. Yousefzadeh, B. Deverakonda, T. Serrano-Gotarredona, and B. Linares-Barranco. Passive localization and detection of quadcopter uavs by using dy- namic vision sensor. In 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pages 81...

    2017

  21. [25]

    Mobilenets: Efficient convolu- tional neural networks for mobile vision applications

    Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco An- dreetto, and Hartwig Adam. Mobilenets: Efficient convolu- tional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017. 4

    2017 arXiv

  22. [26]

    Detection of unmanned aerial vehicles using a visible camera system

    Shuowen Hu, Geoffrey H Goldman, and Christoph C Borel- Donohue. Detection of unmanned aerial vehicles using a visible camera system. Applied optics, 56(3):B214–B221,

  23. [27]

    Event-based motion capture system for on- line multi-quadrotor localization and tracking

    Craig Iaboni, Deepan Lobo, Ji-Won Choi, and Pramod Abichandani. Event-based motion capture system for on- line multi-quadrotor localization and tracking. Sensors, 22 (9):3240, 2022. 2, 5, 7

    2022

  24. [28]

    Temporal binary representa- tion for event-based action recognition

    Simone Undri Innocenti, Federico Becattini, Federico Per- nici, and Alberto Del Bimbo. Temporal binary representa- tion for event-based action recognition. In 2020 25th Inter- national Conference on Pattern Recognition (ICPR) , pages 10426–10432. IEEE, 2021. 4

    2020

  25. [29]

    Anti-uav: A large-scale benchmark for vision-based uav tracking

    Nan Jiang, Kuiran Wang, Xiaoke Peng, Xuehui Yu, Qiang Wang, Junliang Xing, Guorong Li, Qixiang Ye, Jianbin Jiao, Zhenjun Han, et al. Anti-uav: A large-scale benchmark for vision-based uav tracking. T-MM, 2021. 1, 7

    2021

  26. [30]

    Yolov11: An overview of the key architectural enhancements

    Rahima Khanam and Muhammad Hussain. Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725, 2024. 5

    2024 arXiv

  27. [31]

    Uav detection: a stdp trained deep convolutional spiking neural network retina- neuromorphic approach

    Paul Kirkland, Gaetano Di Caterina, John Soraghan, Yiannis Andreopoulos, and George Matich. Uav detection: a stdp trained deep convolutional spiking neural network retina- neuromorphic approach. In International Conference on Ar- tificial Neural Networks, pages 724–736. Spring...

    2019

  28. [32]

    Robust event-based object tracking combining correlation filter and cnn representation

    Hongmin Li and Luping Shi. Robust event-based object tracking combining correlation filter and cnn representation. Frontiers in neurorobotics, 13:82, 2019. 5

    2019

  29. [33]

    Label-free long-horizon 3d uav trajectory prediction via motion-aligned rgb and event cues

    Hanfang Liang, Shenghai Yuan, Fen Liu, Yizhuo Yang, Bing Wang, Zhuyu Huang, Chenyang Shi, and Jing Jin. Label-free long-horizon 3d uav trajectory prediction via motion-aligned rgb and event cues. arXiv preprint arXiv:2507.03365, 2025. 6

    2025 arXiv

  30. [34]

    Edflow: Event driven opti- cal flow camera with keypoint detection and adaptive block matching

    Min Liu and Tobi Delbruck. Edflow: Event driven opti- cal flow camera with keypoint detection and adaptive block matching. IEEE Transactions on Circuits and Systems for Video Technology, 32(9):5776–5789, 2022. 2, 4

    2022

  31. [36]

    Ev-flying: an event-based dataset for in- the-wild recognition of flying objects

    Gabriele Magrini, Federico Becattini, Giovanni Colombo, and Pietro Pala. Ev-flying: an event-based dataset for in- the-wild recognition of flying objects. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 4947–4955, 2025. 3, 4, 7

    2025

  32. [37]

    Spike-tbr: a noise resilient neuromorphic event representa- tion

    Gabriele Magrini, Federico Becattini, Luca Cultrera, Lorenzo Berlincioni, Pietro Pala, and Alberto Del Bimbo. Spike-tbr: a noise resilient neuromorphic event representa- tion. arXiv preprint arXiv:2506.04817, 2025. 4

    2025 arXiv

  33. [40]

    Drone sound detection

    J ´ozsef Mezei, Viktor Fiaska, and Andr ´as Moln ´ar. Drone sound detection. In 2015 16th IEEE International Sym- posium on Computational Intelligence and Informatics (CINTI), pages 333–338. IEEE, 2015. 2

    2015

  34. [41]

    Drone- vs-bird: Drone detection using yolov7 with csrt tracker

    Sahaj K Mistry, Shreyas Chatterjee, Ajeet K Verma, Vinit Jakhetiya, Badri N Subudhi, and Sunil Jaiswal. Drone- vs-bird: Drone detection using yolov7 with csrt tracker. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages...

    2023

  35. [42]

    Event-based moving object detection and tracking

    Anton Mitrokhin, Cornelia Ferm ¨uller, Chethan Paramesh- wara, and Yiannis Aloimonos. Event-based moving object detection and tracking. In2018 IEEE/RSJ International Con- ference on Intelligent Robots and Systems (IROS), pages 1–9. IEEE, 2018. 2, 5, 7

    2018

  36. [43]

    Fast trajectory end-point pre- diction with event cameras for reactive robot control

    Marco Monforte, Luna Gava, Massimiliano Iacono, Arren Glover, and Chiara Bartolozzi. Fast trajectory end-point pre- diction with event cameras for reactive robot control. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4036–4044, 2023. 6

    2023

  37. [44]

    Robust drone detection for day/night counter-uav with static vis and swir cameras

    Thomas M ¨uller. Robust drone detection for day/night counter-uav with static vis and swir cameras. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, pages 302–313. SPIE, 2017. 2

    2017

  38. [45]

    Investigating cost-effective rf-based de- tection of drones

    Phuc Nguyen, Mahesh Ravindranatha, Anh Nguyen, Richard Han, and Tam Vu. Investigating cost-effective rf-based de- tection of drones. In Proceedings of the 2nd workshop on micro aerial vehicle networks, systems, and applications for civilian use, pages 17–22, 2016. 2

    2016

  39. [46]

    Real world object detection dataset for quadcopter unmanned aerial vehicle de- tection

    Maciej Pawełczyk and Marek Wojtyra. Real world object detection dataset for quadcopter unmanned aerial vehicle de- tection. IEEE Access, 8:174394–174409, 2020. 1, 7

    2020

  40. [47]

    Pointnet: Deep learning on point sets for 3d classification and segmentation

    Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660,

  41. [48]

    Pointnet++: Deep hierarchical feature learning on point sets in a metric space

    Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30, 2017. 4

    2017

  42. [49]

    Esim: an open event camera simulator

    Henri Rebecq, Daniel Gehrig, and Davide Scaramuzza. Esim: an open event camera simulator. In Conference on robot learning, pages 969–982. PMLR, 2018. 8

    2018

  43. [50]

    You only look once: Unified, real-time object de- tection

    Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object de- tection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016. 3

    2016

  44. [51]

    Flying objects detection from a single moving camera

    Artem Rozantsev, Vincent Lepetit, and Pascal Fua. Flying objects detection from a single moving camera. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 4128–4136, 2015. 2

    2015

  45. [52]

    Evpropnet: Detecting drones by finding pro- pellers for mid-air landing and following

    Nitin J Sanket, Chahat Deep Singh, Chethan M Paramesh- wara, Cornelia Ferm¨uller, Guido CHE de Croon, and Yiannis Aloimonos. Evpropnet: Detecting drones by finding pro- pellers for mid-air landing and following. arXiv preprint arXiv:2106.15045, 2021. 6

    2021 arXiv

  46. [53]

    Airsim: High-fidelity visual and physical simula- tion for autonomous vehicles

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical simula- tion for autonomous vehicles. In Field and service robotics: Results of the 11th international conference, pages 621–635. Springer, 2017. 8

    2017

  47. [54]

    Hats: Histograms of aver- aged time surfaces for robust event-based object classifica- tion

    Amos Sironi, Manuele Brambilla, Nicolas Bourdis, Xavier Lagorce, and Ryad Benosman. Hats: Histograms of aver- aged time surfaces for robust event-based object classifica- tion. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1731–1740, 2018. 4

    2018

  48. [55]

    Event-based real-time detection and tracking of uavs

    Rikard Frids ´en Skogsberg, Jens Grundmark, and Matts Bj¨orck. Event-based real-time detection and tracking of uavs. In Emerging Imaging and Sensing Technologies for Security and Defence IX, pages 30–40. SPIE, 2024. 5

    2024

  49. [56]

    Efficient real- time quadcopter propeller detection and attribute estimation with high-resolution event camera

    Radim Spetlik, Tereza Uhrov ´a, and Jiˇr´ı Matas. Efficient real- time quadcopter propeller detection and attribute estimation with high-resolution event camera. In Scandinavian Confer- ence on Image Analysis, pages 217–230. Springer, 2025. 6

    2025

  50. [57]

    Drone virtual fence using a neuromorphic camera

    Terrence Stewart, Marc-Antoine Drouin, Guillaume Gagne, and Guy Godin. Drone virtual fence using a neuromorphic camera. In International Conference on Neuromorphic Sys- tems 2021, pages 1–9, 2021. 6

    2021

  51. [58]

    A virtual fence for drones: Efficiently detecting pro- peller blades with a dvxplorer event camera

    Terrence Stewart, Marc-Antoine Drouin, Michel Picard, Frank Billy Djupkep Dizeu, Anthony Orth, and Guillaume Gagn´e. A virtual fence for drones: Efficiently detecting pro- peller blades with a dvxplorer event camera. In Proceedings of the International Conference on Neuromorph...

    2022

  52. [59]

    Using neuromorphic cameras to track quadcopters

    Terrence Stewart, Marc-Antoine Drouin, Michel Picard, Frank Billy Djupkep, Anthony Orth, and Guillaume Gagn ´e. Using neuromorphic cameras to track quadcopters. In Pro- ceedings of the 2023 International Conference on Neuromor- phic Systems, pages 1–5, 2023. 7

    2023

  53. [60]

    Fusing event- based and rgb camera for robust object detection in adverse conditions

    Abhishek Tomy, Anshul Paigwar, Khushdeep S Mann, Alessandro Renzaglia, and Christian Laugier. Fusing event- based and rgb camera for robust object detection in adverse conditions. In 2022 International Conference on Robotics and Automation (ICRA), pages 933–939. IEEE, 2022. 7

    2022

  54. [61]

    Visev- ent: Reliable object tracking via collaboration of frame and event flows

    Xiao Wang, Jianing Li, Lin Zhu, Zhipeng Zhang, Zhe Chen, Xin Li, Yaowei Wang, Yonghong Tian, and Feng Wu. Visev- ent: Reliable object tracking via collaboration of frame and event flows. IEEE Transactions on Cybernetics, 2023. 7, 8

    2023

  55. [62]

    Event stream-based visual object tracking: A high-resolution benchmark dataset and a novel baseline

    Xiao Wang, Shiao Wang, Chuanming Tang, Lin Zhu, Bo Jiang, Yonghong Tian, and Jin Tang. Event stream-based visual object tracking: A high-resolution benchmark dataset and a novel baseline. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, p...

    2024

  56. [63]

    Object detection using event camera: A moe heat conduction based detector and a new benchmark dataset

    Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang, Lin Zhu, Bo Jiang, and Yonghong Tian. Object detection using event camera: A moe heat conduction based detector and a new benchmark dataset. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 29321–29330,

  57. [64]

    Joint filtering of in- tensity images and neuromorphic events for high-resolution noise-robust imaging

    Zihao W Wang, Peiqi Duan, Oliver Cossairt, Aggelos Kat- saggelos, Tiejun Huang, and Boxin Shi. Joint filtering of in- tensity images and neuromorphic events for high-resolution noise-robust imaging. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Rec...

    2020

  58. [65]

    Vision-based real-time aerial object localization and tracking for uav sens- ing system

    Yuanwei Wu, Yao Sui, and Guanghui Wang. Vision-based real-time aerial object localization and tracking for uav sens- ing system. IEEE Access, 5:23969–23978, 2017. 2

    2017

  59. [66]

    Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

    Man Yao, Ole Richter, Guangshe Zhao, Ning Qiao, Yan- nan Xing, Dingheng Wang, Tianxiang Hu, Wei Fang, Tugba Demirci, Michele De Marchi, Lei Deng, Tianyi Yan, Carsten Nielsen, Sadique Sheik, Chenxi Wu, Yonghong Tian, Bo Xu, and Guoqi Li. Spike-based dynamic computing with async...

    2024

  60. [67]

    Drone and controller detection and localization: Trends and challenges

    Jawad Yousaf, Huma Zia, Marah Alhalabi, Maha Yaghi, Tas- nim Basmaji, Eiman Al Shehhi, Abdalla Gad, Mohammad Alkhedher, and Mohammed Ghazal. Drone and controller detection and localization: Trends and challenges. Applied Sciences, 12(24):12612, 2022. 1

    2022

  61. [68]

    Spiking swin transformer for uav object detection based on event cam- eras

    Heng Zhang, Nuo Chen, Miao Li, and Wei An. Spiking swin transformer for uav object detection based on event cam- eras. In 2024 12th International Conference on Information Systems and Computing Technology (ISCTech) , pages 1–6. IEEE, 2024. 5, 6

    2024

  62. [69]

    Spiking transform- ers for event-based single object tracking

    Jiqing Zhang, Bo Dong, Haiwei Zhang, Jianchuan Ding, Fe- lix Heide, Baocai Yin, and Xin Yang. Spiking transform- ers for event-based single object tracking. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, pages 8801–8810, 2022. 5

    2022

  63. [70]

    Bytetrack: Multi-object tracking by associating every detection box

    Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Bytetrack: Multi-object tracking by associating every detection box. In European conference on computer vision, pages 1–21. Springer, 2022. 5

    2022

  64. [71]

    High speed rotation estimation with dynamic vision sensors

    Guangrong Zhao, Yiran Shen, Ning Chen, Pengfei Hu, Lei Liu, and Hongkai Wen. High speed rotation estimation with dynamic vision sensors. arXiv preprint arXiv:2209.02205 ,

    arXiv

  65. [72]

    Ev-tach: A handheld rotational speed estimation system with event camera

    Guangrong Zhao, Yiran Shen, Ning Chen, Pengfei Hu, Lei Liu, and Hongkai Wen. Ev-tach: A handheld rotational speed estimation system with event camera. IEEE Trans- actions on Mobile Computing, 23(6):7483–7498, 2023. 6

    2023

  66. [73]

    Detrs beat yolos on real-time object detection

    Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen. Detrs beat yolos on real-time object detection. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16965–16974, 2024. 5

    2024

  67. [74]

    The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception

    Alex Zihao Zhu, Dinesh Thakur, Tolga ¨Ozaslan, Bernd Pfrommer, Vijay Kumar, and Kostas Daniilidis. The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception. IEEE Robotics and Automation Letters, 3 (3):2032–2039, 2018. 4

  68. [75]

    Unsupervised event-based learning of optical flow, depth, and egomotion

    Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, and Kostas Daniilidis. Unsupervised event-based learning of optical flow, depth, and egomotion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 989–997, 2019. 4

    2019

  69. [76]

    Crsot: Cross-resolution object tracking using unaligned frame and event cameras

    Yabin Zhu, Xiao Wang, Chenglong Li, Bo Jiang, Lin Zhu, Zhixiang Huang, Yonghong Tian, and Jin Tang. Crsot: Cross-resolution object tracking using unaligned frame and event cameras. arXiv preprint arXiv:2401.02826, 2024. 7, 8

    2024 arXiv

  70. [77]

    Collision avoidance for uav using visual detection

    Tam ´as Zsedrovits, ´Akos Zar´andy, B´alint Vanek, Tam´as P´eni, J´ozsef Bokor, and Tam ´as Roska. Collision avoidance for uav using visual detection. In 2011 IEEE International Sym- posium of Circuits and Systems (ISCAS) , pages 2173–2176. IEEE, 2011. 2

    2011

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.