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REVIEW 3 major objections 4 minor 1 cited by

RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper introduces RadDet, a public dataset of 40,000 synthetic wideband radar frames spanning 500 MHz with 11 radar classes, and demonstrates real-time YOLO-family detectors localising radar emissions at hundreds of frames per second.

desk verdict A genuinely useful wideband radar dataset, but the printed annotation formula is dimensionally wrong and that must be fixed before the dataset can be trusted. read the letter →

arxiv 2501.10407 v1 pith:EBLSFSSD submitted 2025-01-06 eess.SP

Zi Huang , Simon Denman , Akila Pemasiri , Terrence Martin , Clinton Fookes This is my paper · ORCID
classification eess.SP
keywords radarspectrumdetectionwidebanddatasetreal-timetime-frequencyannotationsYOLOspectrogramlowprobabilityofinterceptelectronicwarfare
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 tries to remove a data bottleneck: radar spectrum detection research lacks public wideband datasets with time-frequency annotations, so models cannot be compared or trained in realistic dense environments. RadDet provides 40,000 frames, each generated from one million I/Q samples across a 500 MHz band, covering 11 radar classes, six SNR settings, two radar-density environments, and three time-frequency resolutions. The authors train standard real-time object detectors on the spectrogram frames and report detection accuracy and speed, showing that the task is feasible at hundreds of frames per second. If the synthetic signals faithfully represent non-cooperative radar environments, RadDet becomes a repeatable benchmark for a capability that previously had no common testbed.

What carries the argument

The engine is the max-hold spectrogram pipeline: each frame's one million complex I/Q samples are transformed with a short-time Fourier transform, and a max-hold pooling step compresses the time axis while preserving bright radar returns. Bounding boxes are computed from the known radar parameters through the paper's equations (1)-(3), so each radar instance gets a time-frequency box in YOLO format. The dataset is offered in sparse (at most one emitter per frame) and dense (up to nine emitters per frame) variants, and the benchmark models are standard real-time detectors (YOLOv3, YOLOv6, YOLOv9, RT-DETR) trained on these spectrogram images.

What would settle it

Take the trained RadDet detectors and run them on a real recorded wideband radar capture, digitised I/Q from a maritime or airborne setting with operator-confirmed radar annotations, and compare per-class mAP50 to the synthetic test set. If real-world scores fall far outside the 20-60% mAP50 range reported here, the benchmark's realism assumption is falsified.

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Extended reading notes

Core claim

The central claim is that RadDet is a wideband radar detection benchmark with bounding-box-level annotations for radar dwells, going beyond narrowband or single-instance datasets. On the dense version, at the finest resolution, the strongest YOLO-family model reaches about 60% mAP50, while the sparse version is much harder, with mAP50 near 20-30%; inference speeds range from roughly 200 to over 1000 frames per second depending on resolution and model. The paper also converts the 3.5 GHz CBRS radar dataset into a detection dataset with computed time-frequency boxes, serving as a baseline. The discovery is that real-time object detection recipes transfer to spectrogram images well enough to localise multiple overlapping radar signals, with augmentation and resolution as the main controls on the accuracy-speed trade-off.

Load-bearing premise

The whole benchmark stands on the premise that the synthetic radar signals, with parameters drawn from an emitter library and noise added as AWGN, are representative enough of real non-cooperative radar environments for measured performance to transfer.

Editorial extensions

If this is right

  • Labs working on radar detection can now compare models on identical frames instead of private datasets.
  • Deployable wideband monitoring is plausible: at the smallest input resolution, detectors run at over 1000 frames per second while still detecting signals.
  • Time-frequency resolution is a tunable operating point: moving from 128x128 to 512x512 inputs roughly doubles dense-scene mAP50 but cuts frame rate by about a factor of four.
  • Sparse radar environments expose a concrete weakness in current detectors: small LPI waveforms and rare pulses keep mAP50 below 35%.

Reading between the lines

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

  • My inference: the dataset's realism is the unvalidated link; an obvious next experiment is to record a small set of real over-the-air LPI radar emissions and measure how much the detector's mAP drops on those recordings.
  • My inference: because frames are synthetic and parameterised, the same generation pipeline could produce unlimited variants, so a follow-up could expand SNR ranges, add interference, or add new classes without recollecting data.
  • My inference: the bounding-box convention treats each radar dwell as one object, but pulse-level segmentation would be a natural harder task that the same dataset could support if masks were generated.
  • My inference: the max-hold pooling factor controls the accuracy-speed trade-off; making that factor adaptive per frame or per frequency band might recover accuracy in dense scenes without sacrificing real-time speed.
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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

3 major / 4 minor

Summary. This paper introduces RadDet, a synthetic open-source wideband radar spectrum detection dataset with 40,000 frames, each derived from 1 million I/Q samples across a 500 MHz band, covering 11 radar classes, 6 SNR settings, sparse and dense environments, and 3 time-frequency resolutions. The authors also adapt the NIST-CBRS dataset into a detection benchmark and evaluate YOLOv3, YOLOv6, YOLOv9, and RT-DETR with mAP and FPS metrics. The central claim is that RadDet provides a large-scale, publicly available benchmark with time-frequency and class annotations for real-time radar spectrum detection, and that real-time detectors can operate on it at hundreds of frames per second.

Significance. If the dataset and annotations are correct and the code and data are actually released, RadDet fills a genuine gap: public wideband radar detection datasets with dense multi-emitter frames, localisation annotations, and controlled SNR and density variations are scarce. The paper's strengths include the large scale, the systematic variation of SNR, density, and time-frequency resolution, the use of standard detection architectures, and the detailed training protocol. However, the annotation formula in Eq. (1) is dimensionally inconsistent, making the correctness of the ground-truth boxes unverifiable; the repository link is a placeholder; and the synthetic-to-real transferability is asserted rather than validated. These issues are load-bearing for the dataset's central contribution and must be resolved before the benchmark can be used as a reference.

major comments (3)
  1. [Section III-A, Eq. (1)] Equation (1) specifies the temporal bounding box as [ts, ts + N/fprf], with N stated to be the sample length. For RadDet, N=10^6 and fprf ranges over 10-50 kHz (Table I), so the formula gives widths of 20-100 seconds, whereas each frame is only N/fs = 2 ms long. This is dimensionally inconsistent: a number of samples divided by a frequency does not yield a time interval, and it cannot describe the temporal extent of a pulse train, which should be roughly (Npulse-1)/fprf + tpw. Since Section III-B states that all annotations are computed per Eqs. (1)-(2), either the released dataset was annotated with a different rule or the paper mis-specifies the rule. I request a corrected formula (e.g., using Npulse or an explicit pulse-train duration), release of the annotation-generation code, and confirmation that the Table III results were produced with the corrected annotations.
  2. [Section I, footnote 1] The paper claims the dataset is open-source, but the URL https://github.com/abcxyzi/RadDet is a placeholder. The central contribution of the paper is the dataset itself, so a working repository containing the full dataset, generation scripts, and annotation code is necessary for the claims to be verifiable. Please provide the actual URL and ensure it is live at the time of publication.
  3. [Section III-B, emitter parameter realism] The paper asserts that the emitter parameter library from [32] bounds the sampling range and that AWGN models the noise environment, but it provides no comparison with measured radar signals, no channel effects beyond AWGN, and no independent sanity check of the generated signals. Because the utility of the benchmark depends on transfer to real non-cooperative radar environments, the authors should at least state this limitation explicitly and, if possible, include a small validation set of real or independently generated signals to support the realism claim.
minor comments (4)
  1. [Section III-B] The word 'genreated' should be 'generated'.
  2. [Table I] The parameter name 'tprf (kHz)' is inconsistent with Eq. (1)'s 'fprf'; rename it to fprf or PRF for consistency.
  3. [Eq. (1)] Equation (1) calls tbbox the bbox width but writes it as an interval; clarify that the interval denotes the time range and that the width is the length of that interval.
  4. [Table II] The dimensions '1282', '2562', and '5122' appear to be missing superscripts (128^2, 256^2, 512^2); fix the formatting.

Circularity Check

0 steps flagged · score 2.0 of 10

No circularity: the benchmark is empirical, annotations are dataset definitions, and self-citations are non-load-bearing.

full rationale

The paper's central contribution is a dataset and a benchmark, not a derived physical prediction. The ground-truth annotations are generated from the same signal parameters used to synthesize the radar frames; this is by design for a dataset and does not constitute circularity, because the benchmark scores are measured empirically on held-out test frames using external public detectors (YOLOv3/6/9, RT-DETR) and are not derived from the dataset definition. Signal parameter ranges are drawn from an external emitter parameter library [32], and the NIST-CBRS baseline uses external data [28], [29]. The only self-citations ([18], [20], [22]) are contextual related-work references; 'RadDet builds upon [20]' describes lineage but does not justify any load-bearing claim. The dimensional concern about Eq. (1), where t_bbox = N/fprf yields widths far exceeding the 2 ms frame, is a correctness and verifiability issue rather than a circularity: it does not make any result equivalent to its inputs by construction. No fitted constant is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The score of 2 reflects the presence of minor self-citations in the related-work and dataset lineage, but none of them is load-bearing, so there is no significant circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

RadDet is a new data artifact but not a new physical entity, so invented_entities is empty. The central claim depends on one hand-chosen annotation constant (epsilon) and on three domain assumptions about realism of synthetic signals, AWGN modeling, and max-hold spectrogram sufficiency.

free parameters (1)
  • epsilon (pulse bandwidth estimate buffer) = 0.5 * t_pw^-1
    Introduced by hand in Eq. (3) to account for uncertainty in pulsed and phase-coded radar bandwidth when computing annotation bbox height. It changes the frequency extent of annotations and is not derived from data.
assumptions (3)
  • domain assumption Emitter parameter ranges from [32] are representative of real non-cooperative radar emitters.
    Invoked in Section III-B to bound random sampling of signal parameters for all radar classes; if the library is unrepresentative, the synthetic dataset has limited transfer value.
  • domain assumption Additive white Gaussian noise and synthetic waveforms adequately model the radar spectrum detection problem.
    Stated in Section III-B: AWGN is added to simulate SNR. There is no multipath, clutter, interference, or hardware impairment, and the paper does not validate against measured wideband radar data.
  • domain assumption Max-hold STFT spectrograms preserve enough temporal and spectral information for detection.
    Used in Section III-B to compress long I/Q sequences into images; the paper presents it as a necessary trade-off but does not compare detection on max-hold images against raw I/Q or other transforms.

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Cite this review

Pith. "Pith review of RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection." pith.science (2026). https://pith.science/paper/EBLSFSSD

@misc{pith2026250110407,
  author       = {Pith},
  title        = {Pith review of: RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EBLSFSSD}},
  note         = {Machine review of arXiv:2501.10407}
}
read the original abstract

Real-time detection of radar signals in a wideband radio frequency spectrum is a critical situational assessment function in electronic warfare. Compute-efficient detection models have shown great promise in recent years, providing an opportunity to tackle the spectrum detection problem. However, progress in radar spectrum detection is limited by the scarcity of publicly available wideband radar signal datasets accompanied by corresponding annotations. To address this challenge, we introduce a novel and challenging dataset for radar detection (RadDet), comprising a large corpus of radar signals occupying a wideband spectrum across diverse radar density environments and signal-to-noise ratios (SNR). RadDet contains 40,000 frames, each generated from 1 million in-phase and quadrature (I/Q) samples across a 500 MHz frequency band. RadDet includes 11 classes of radar samples across 6 different SNR settings, 2 radar density environments, and 3 different time-frequency resolutions, with corresponding time-frequency and class annotations. We evaluate the performance of various state-of-the-art real-time detection models on RadDet and a modified radar classification dataset from NIST (NIST-CBRS) to establish a novel benchmark for wideband radar spectrum detection.

Figures

Figures reproduced from arXiv: 2501.10407 by the authors.

Figure 1
Figure 1. Radar detection frames with time-frequency annotations (bounding [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Radar detection results at 10 dB SNR. Ground truth frames in (a)–(c) correspond to model predictions in (d)–(f), respectively. The confidence scores of each bounding box prediction are also shown in (d)–(f). TABLE II RADAR DETECTION DATASET VARIANTS Dataset Size Npool Dim. Res. (µs, MHz) NIST-CBRS S 60 1282 624, 0.08 (fs = 10 MHz) M 30 2562 313, 0.04 L 15 5122 157, 0.02 RadDet-9T/1T S 75 1282 15.6, 3.91 (fs = 500 MH… view at source ↗
Figure 3
Figure 3. Comparison of detection performance of models evaluated on NIST [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 2
Figure 2. Figure 2: This challenge stems from two key factors: the presence [PITH_FULL_IMAGE:figures/full_fig_p004_2.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

39 extracted references · 34 canonical work pages · cited by 1 Pith paper

  1. [32]

    Lynch Jr., An Introduction to RF Stealth

    David. Lynch Jr., An Introduction to RF Stealth. , Radar, Sonar and Navigation. Institution of Engineering & Technology, Stevenage, 2nd ed. edition, 2021

    work page 2021

  2. [1]

    Sue Robertson, Practical ESM Analysis , Artech House, 2019

    work page 2019

  3. [2]

    Karen Haigh and Julia Andrusenko, Cognitive Electronic Warfare: An Artificial Intelligence Approach , Artech House, 2021

    work page 2021

  4. [3]

    Wideband Spec- trum Sensing Technique Based on Goodness-of-Fit Testing,

    Bart Scheers, Djamel Teguig, and Vincent Le Nir, “Wideband Spec- trum Sensing Technique Based on Goodness-of-Fit Testing,” in 2015 International Conference on Military Communications and Information Systems (ICMCIS). IEEE, 2015, pp. 1–6

    work page 2015

  5. [4]

    Kurtosis-Based Approach for Detection of Targets in Noise,

    Alexander Schmidt, Christoph R ¨ugheimer, Florian Particke, Thomas Mahr, Holger Appel, and Hans-Georg K ¨olle, “Kurtosis-Based Approach for Detection of Targets in Noise,” in 2016 17th International Radar Symposium (IRS). IEEE, 2016, pp. 1–3

    work page 2016

  6. [5]

    Con- volutional Radio Modulation Recognition Networks,

    Timothy J. O’Shea, Johnathan Corgan, and T. Charles Clancy, “Con- volutional Radio Modulation Recognition Networks,” in Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. 2016, pp. 213– 226, Springer

    work page 2016

  7. [6]

    Unsu- pervised Representation Learning of Structured Radio Communication Signals,

    Timothy J. O’Shea, Johnathan Corgan, and T. Charles Clancy, “Unsu- pervised Representation Learning of Structured Radio Communication Signals,” in 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) . 2016, pp. 1–5, IEEE

    work page 2016

  8. [7]

    Over-the- Air Deep Learning Based Radio Signal Classification,

    Timothy J. O’Shea, Tamoghna Roy, and T. Charles Clancy, “Over-the- Air Deep Learning Based Radio Signal Classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179, 2018, IEEE

    work page 2018

Show all 39 references
  1. [8]

    Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge,

    Andres Vila, Donna Branchevsky, Kyle Logue, Sebastian Olsen, Esteban Valles, Darren Semmen, Alex Utter, and Eugene Grayver, “Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge,” in Proceedings of the 18th Python in Science Conference , 2019, pp. 21–26

    2019

  2. [9]

    Expert RF Feature Extraction to Win the Army RCO AI Signal Classification Challenge,

    Kyle Logue, Esteban Valles, Andres Vila, Alex Utter, Darren Semmen, Eugene Grayver, Sebastian Olsen, and Donna Branchevsky, “Expert RF Feature Extraction to Win the Army RCO AI Signal Classification Challenge,” in Proceedings of the 18th Python in Science Conference , 2019, pp. 8–14

    2019

  3. [10]

    Automatic Modulation Classification: A Deep Architecture Survey,

    Thien Huynh-The, Quoc-Viet Pham, Toan-Van Nguyen, Thanh Thi Nguyen, Rukhsana Ruby, Ming Zeng, and Dong-Seong Kim, “Automatic Modulation Classification: A Deep Architecture Survey,” IEEE Access, vol. 9, pp. 142950–142971, 2021

    2021

  4. [11]

    Learning Robust General Radio Signal Detection Using Computer Vision Methods,

    Tim O’Shea, Tamoghna Roy, and T. Charles Clancy, “Learning Robust General Radio Signal Detection Using Computer Vision Methods,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers . IEEE, 2017, pp. 829–832

    2017

  5. [12]

    Augmenting Radio Signals With Wavelet Transform for Deep Learning-Based Modulation Recognition,

    Tao Chen, Shilian Zheng, Kunfeng Qiu, Luxin Zhang, Qi Xuan, and Xiaoniu Yang, “Augmenting Radio Signals With Wavelet Transform for Deep Learning-Based Modulation Recognition,” IEEE Transactions on Cognitive Communications and Networking , 2024

    2024

  6. [13]

    WaveNet: Towards Waveform Classification in Integrated Radar-Communication Systems with Improved Accuracy and Reduced Complexity,

    Thien Huynh-The, Van-Phuc Hoang, Jae-Woo Kim, Minh-Thanh Le, and Ming Zeng, “WaveNet: Towards Waveform Classification in Integrated Radar-Communication Systems with Improved Accuracy and Reduced Complexity,” IEEE Internet of Things Journal , 2024

    2024

  7. [14]

    Accurate LPI Radar Wave- form Recognition With CWD-TFA for Deep Convolutional Network,

    Thien Huynh-The, Van-Sang Doan, Cam-Hao Hua, Quoc-Viet Pham, Toan-Van Nguyen, and Dong-Seong Kim, “Accurate LPI Radar Wave- form Recognition With CWD-TFA for Deep Convolutional Network,” IEEE Wireless Communications Letters , vol. 10, no. 8, pp. 1638–1642, 2021

    2021

  8. [15]

    Fourier, Gabor, Morlet, or Wigner: Comparison of Time- Frequency Transforms,

    Stefan Scholl, “Fourier, Gabor, Morlet, or Wigner: Comparison of Time- Frequency Transforms,” arXiv Preprint arXiv:2101.06707 , 2021

    2021 arXiv

  9. [16]

    A Survey on Spectrum Sensing Algorithms for Cognitive Radio Networks,

    Ioanna Kakalou, Danai Papadopoulou, Theofanis Xifilidis, Kostas E. Psannis, Katherine Siakavara, and Yutaka Ishibashi, “A Survey on Spectrum Sensing Algorithms for Cognitive Radio Networks,” in 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCA...

    2018

  10. [17]

    AST: Audio Spectrogram Transformer,

    Yuan Gong, Yu-An Chung, and James Glass, “AST: Audio Spectrogram Transformer,” July 2021, arXiv:2104.01778 [cs]

    2021 arXiv

  11. [18]

    Multi-Task Learning For Radar Signal Characterisation,

    Zi Huang, Akila Pemasiri, Simon Denman, Clinton Fookes, and Terrence Martin, “Multi-Task Learning For Radar Signal Characterisation,” in 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023, pp. 1–5

    2023

  12. [19]

    Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification,

    Anu Jagannath and Jithin Jagannath, “Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification,” in ICC 2021-IEEE International Conference on Communications . 2021, pp. 1– 7, IEEE

    2021

  13. [20]

    Multi-Stage Learning for Radar Pulse Activity Segmentation,

    Zi Huang, Akila Pemasiri, Simon Denman, Clinton Fookes, and Terrence Martin, “Multi-Stage Learning for Radar Pulse Activity Segmentation,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 7340–7344

    2024

  14. [21]

    Deep Learning Classification of 3.5-GHz Band Spectrograms With Applications to Spectrum Sensing,

    W. Max Lees, Adam Wunderlich, Peter J. Jeavons, Paul D. Hale, and Michael R. Souryal, “Deep Learning Classification of 3.5-GHz Band Spectrograms With Applications to Spectrum Sensing,” IEEE Transactions on Cognitive Communications and Networking , vol. 5, no. 2, pp. 224–236, 2019

    2019

  15. [22]

    Automatic Radar Signal Detection and FFT Estimation Using Deep Learning,

    Akila Pemasiri, Zi Huang, Fraser Williams, Ethan Goan, Simon Den- man, Terrence Martin, and Clinton Fookes, “Automatic Radar Signal Detection and FFT Estimation Using Deep Learning,” arXiv Preprint arXiv:2402.19073, 2024

    2024 arXiv

  16. [23]

    Deep Learning for Radar: A Survey,

    Zhe Geng, He Yan, Jindong Zhang, and Daiyin Zhu, “Deep Learning for Radar: A Survey,” IEEE Access, vol. 9, pp. 141800–141818, 2021

    2021

  17. [24]

    LPI Radar Waveform Recognition Based on CNN and TPOT,

    Jian Wan, Xin Yu, and Qiang Guo, “LPI Radar Waveform Recognition Based on CNN and TPOT,” Symmetry, vol. 11, no. 5, pp. 725, 2019

    2019

  18. [25]

    LSTM Framework for Classification of Radar and Communications Signals,

    Victoria Clerico, Jorge Gonz ´alez-L´opez, Gady Agam, and Jes ´us Grajal, “LSTM Framework for Classification of Radar and Communications Signals,” in 2023 IEEE Radar Conference (RadarConf23) . IEEE, 2023, pp. 1–6

    2023

  19. [26]

    Phillip E Pace, Detecting and Classifying Low Probability of Intercept Radar, Artech House, 2009

    2009

  20. [27]

    Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression,

    Anu Jagannath and Jithin Jagannath, “Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression,” June 2022, arXiv:2203.00517 [cs, eess]

    2022 arXiv

  21. [28]

    RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band,

    Raied Caromi, Michael Souryal, and Timothy A. Hall, “RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band,” Journal of Research of the National Institute of Standards and Technology , vol. 124, pp. 1, 2019

    2019

  22. [29]

    Deep Learning for Radar Signal Detection in the 3.5 GHz CBRS Band,

    Raied Caromi, Alex Lackpour, Kassem Kallas, Thao Nguyen, and Michael Souryal, “Deep Learning for Radar Signal Detection in the 3.5 GHz CBRS Band,” in 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) . IEEE, 2021, pp. 1–8

    2021

  23. [30]

    Com- bined RF-Based Drone Detection and Classification,

    Sanjoy Basak, Sreeraj Rajendran, Sofie Pollin, and Bart Scheers, “Com- bined RF-Based Drone Detection and Classification,” IEEE Transactions on Cognitive Communications and Networking , vol. 8, no. 1, pp. 111– 120, 2021

    2021

  24. [31]

    Finding waldo in the cbrs band: Signal detection and localization in the 3.5 ghz spectrum,

    Nasim Soltani, Vini Chaudhary, Debashri Roy, and Kaushik Chowdhury, “Finding waldo in the cbrs band: Signal detection and localization in the 3.5 ghz spectrum,” in GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022, pp. 4570–4575

    2022

  25. [33]

    You Only Look Once: Unified, Real-Time Object Detection,

    Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 779–788

    2016

  26. [34]

    YOLOv3: An Incremental Improve- ment,

    Joseph Redmon and Ali Farhadi, “YOLOv3: An Incremental Improve- ment,” arXiv Preprint arXiv:1804.02767 , 2018

    2018 arXiv

  27. [35]

    YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,

    Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, et al., “YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,” arXiv Preprint arXiv:2209.02976 , 2022

    2022 arXiv

  28. [36]

    YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,

    Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” arXiv Preprint arXiv:2402.13616 , 2024

    2024 arXiv

  29. [37]

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16965–16974

    2024

  30. [38]

    Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation,

    Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph, “Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,...

    2021

  31. [39]

    The PASCAL Visual Object Classes (VOC) Challenge,

    Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman, “The PASCAL Visual Object Classes (VOC) Challenge,” International Journal of Computer Vision, vol. 88, pp. 303– 338, 2010

    2010

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