The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Using deep networks for drone detection
Open accessAbstract
Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“Detection with Handcrafted Features: The most successful approaches using handcrafted features require bag of visual words (BoVW) [16] representations of the objects with the help of local feature descriptors such as scale invariant feature transform (SIFT) [9], speeded-up robust features (SURF) [1], and histogram of…”
MethodVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Cited by
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research
Taha, B., & Shoufan, A. (2019) · IEEE Access · doi:10.1109/access.2019.2942944 · Cited by 375
Survey on Anti-Drone Systems: Components, Designs, and Challenges
Park, S., Kim, H. T., Lee, S., Joo, H., & Kim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3065926 · Cited by 325
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies
Lykou, G., Moustakas, D., & Gritzalis, D. (2020) · Sensors · doi:10.3390/s20123537 · Cited by 268
A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
Castrillo, V. U., Manco, A., Pascarella, D., & Gigante, G. (2022) · Drones · doi:10.3390/drones6030065 · Cited by 131
Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection
Pawelczyk, M. L., & Wojtyra, M. (2020) · IEEE Access · doi:10.1109/access.2020.3026192 · Cited by 121
Metadata
- Authors
- Aker, C., & Kalkan, S.
- Venue
- 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 1-6
- OpenAlex
- W2718471533
- Cited by
- 202
- References
- 7
- FWCI
- 5.74
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using deep networks for drone detection. https://app.cochat.ai/papers/10.1109/avss.2017.8078539
- The paper
- Aker, C., & Kalkan, S. (2017). Using deep networks for drone detection. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). https://doi.org/10.1109/avss.2017.8078539
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
