Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge
Paper • 2405.16464 • Published
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MMAUD
A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats
Camera · LiDAR · mmWave radar · Audio · Leica ground truth
MMAUD brings complementary sensing modalities together for drone detection, classification, tracking and trajectory estimation. This repository provides the recordings, extracted data, calibration resources and practical guides in one public download location.
📄 Paper · PDF · 🌐 Project · 💻 GitHub · 📦 Browse data
Overview · Versions · Download · Quick start · Baselines · Calibration · Citation
Camera images, LiDAR and radar point clouds, and audio from the original MMAUD project. Figure source.
Dataset at a glance
✅ Upload complete and verified — approximately 367.86 GB, with original filenames and public downloads.
| Resource | Included in this release |
|---|---|
| Sensor recordings | 12 ROS1 bags with bz2 chunk compression |
| Ground truth and calibration recordings | 13 bags |
| Extracted V1 data | 76,786 individual files: 33,200 PNG images + 43,586 NumPy arrays |
| Other source files | 43 files: 28 WAV, 10 extraction scripts, 3 CSV, noise.bin and the original point-cloud ZIP |
| Calibration resources | 20 files, including camera parameters, reports, CAD/STL and the full 7.24 GB calibration archive |
| Getting started | 11 baseline code entries, a YOLOv5 starting point, 18 common-issue answers and an index of 69 GitHub issues |
All 76,829 supplementary source files are included. Images and NumPy arrays can be browsed and downloaded individually, preserving their sequence, modality and timestamp filenames. Sizes on this page use decimal GB.
Dataset versions
| Version | Role | Scenario difficulty | Typical localization error |
|---|---|---|---|
| V1 | The official dataset contribution of the ICRA 2024 paper | Relatively easier | 0.3–1 m |
| V2 / V3 | CVPR 2025 workshop challenge extensions | More challenging | 1–10 m is common |
These error ranges are practical guidance from the dataset maintainer, rather than scores for a single benchmark method. Results vary with the method, sequence and evaluation setup.
The source collection groups V2 and V3 under V2_V3/. The maintainer's filename clarification assigns s1 to V2, s2 to V3, and m3e to V2. Original filenames and directory grouping are preserved.
Download
Choose the format that fits your workflow: ROS bags for replaying sensor streams, PNG/NPY files for direct processing, or WAV files for audio experiments.
| Data | Download / browse | Contents |
|---|---|---|
| V1 sensor recordings | V1/rosbag | Avata, M300, Mavic2, Mavic3, Pham4 |
| V1 ground truth | V1/ground_truth | Five corresponding GT bags |
| V1 extracted files | V1/filesystem_format | Images, LiDAR, radar and ground-truth arrays |
| V2 / V3 sensor recordings | V2_V3/rosbag | m300s1, m300s2, m30s1, m30s2, m3e, p4s1, p4s2 |
| V2 / V3 ground truth | V2_V3/ground_truth | Seven GT bags and calibration.bag |
| Extracted audio | V2_V3/audio | Four WAV files per sequence; 28 in total |
| Original extraction scripts | V1 scripts · V2 / V3 scripts | Ten source scripts; usage notes in Common Issues |
| Supplementary files | supplementary | Interpolated CSVs, noise.bin and original 点云.zip |
| Calibration and sensor geometry | calibration | Camera YAMLs, reports, raw calibration archive and CAD |
Large recording: the 54.74 GB m3e.bag exceeds the Hub's 50 GB per-file limit, so it is provided as three ordered byte parts and a .bag.parts.json manifest. The reassembly script restores the original bag byte for byte and checks its SHA-256. Follow the download and reassembly guide.
See File layout and source coverage for per-sequence counts and source-to-repository mapping. The separate challenge train.zip, val.zip and 59-sequence test.zip are outside this source collection; their references are in Common Issues.
Quick start
Download a selected sequence
Install the Hugging Face Hub client:
pip install -U huggingface_hub
This example downloads the extracted V1 Mavic3 sequence into a local MMAUD/ folder:
import os
os.environ["HF_HUB_DISABLE_XET"] = "1"
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Yuan-Shenghai/MMAUD",
repo_type="dataset",
allow_patterns="V1/filesystem_format/Mavic3/*",
local_dir="MMAUD",
)
Use V2_V3/audio/* for audio or V1/rosbag/* for the V1 sensor bags. A single file can also be downloaded from its page using the Download button. The full Mavic3 extracted sequence is approximately 13.04 GB.
Inspect and replay a ROS bag
The sensor recordings use ROS1 bag format with bz2 chunk compression. With ROS Noetic installed, inspect a downloaded bag, decompress it for playback, then replay it:
rosbag info Mavic3.bag
rosbag decompress Mavic3.bag
rosbag play Mavic3.bag
Allow space for decompression: the five V1 sensor recordings occupy approximately 77.88 GB compressed and 298.29 GB uncompressed.
Sensor setup
The collection platform combines two cameras, two 3D LiDAR sensors, mmWave radar and four audio streams, with Leica tracking for ground truth.
Original sensing rig, Leica tracking and example detection, tracking and classification tasks. Figure source. Sensor geometry and transform usage are documented in the calibration guide.
Baseline code
Choose a baseline for your sensor setup. The catalogue includes the original 11 MMAUD baselines, 5 additional methods, pipelines or backbones, 4 detector families, and 5 data or evaluation tools.
📖 Baseline guide · 📚 Papers and BibTeX · 🔎 Code and source audit
The guide covers installation, input preparation, training/evaluation entry points and licenses. Paper links accompany research methods; software references identify community implementations and tools. Check the required annotations, pretrained weights and calibration before running a pipeline.
Original MMAUD baselines
| Method / resource | Sensor input / purpose | Code / setup | Paper / reference |
|---|---|---|---|
| ResNet | Images | Code · Comparison model | Paper · BibTeX |
| Darknet | Images | Code · Comparison model | Paper · BibTeX |
| VorasNet / DroneChase | Audio | Code · Comparison model | Paper · BibTeX |
| AV-PED | Audio + images | Code · Comparison model | Paper · BibTeX |
| Audionet | Audio | Code · Comparison model | Paper · BibTeX |
| AV-FDTI | Audio + images | Code | Paper · BibTeX |
| TAME | Audio | Code | Paper · BibTeX |
| AAUTE | Audio | Code | Paper · BibTeX |
| UnLiDAR | LiDAR | Code | Paper · BibTeX |
| MMUDCT | LiDAR + images | Code | Paper · BibTeX |
| AV-DTEC | Audio + images; LiDAR-derived supervision | Code | Paper · BibTeX |
AV-PED and Audionet share the AV-PedAware reference. The AV-DTEC code repository is named AV-DETC.
Additional public methods and implementations
These entries cover multimodal classification, trajectory estimation and feature extraction. PointNeXt-S is a point-cloud backbone used by the LMMUAVC pipeline.
| Method / resource | Sensor input / purpose | Code / setup | Paper / reference |
|---|---|---|---|
| LMMUAVC — lightweight fusion | RGB + Livox Avia + 360° LiDAR + radar | Code · Entry point | Software DOI · BibTeX |
| TARF | Multiple LiDAR streams | Code · Entry point | Software notes · BibTeX |
| MMAUD acoustic Transformer | Four-channel audio | Code · Entry point | Software notes · BibTeX |
| YOLOv8 + geometry + Kalman tracking | Stereo or monocular RGB | Code · Entry point | Software notes · BibTeX |
| PointNeXt-S | LiDAR point clouds / object crops | Code · Entry point | Paper · BibTeX |
2D image detection
Use the annotated 2D subset with the selected framework’s custom-data training workflow. Declare the sequence split and provide drone labels or trained weights. These detector frameworks require MMAUD-specific training and preprocessing.
| Method / resource | Sensor input / purpose | Code / setup | Paper / reference |
|---|---|---|---|
| YOLOv5 | RGB images + 2D training boxes | Code | Software notes · BibTeX |
| YOLOv8 | RGB images + 2D training boxes | Code · Entry point | Software notes · BibTeX |
| YOLOv10 | RGB images + 2D training boxes | Code · Entry point | Paper · BibTeX |
| YOLO11 | RGB images + 2D training boxes | Code · Entry point | Software notes · BibTeX |
Data preparation, annotation and evaluation tools
GT-assisted tools support annotation and evaluation; keep ground truth out of sensor-only inference. The simulation and filtering tools below serve synthetic-data and trajectory studies.
| Method / resource | Sensor input / purpose | Code / setup | Paper / reference |
|---|---|---|---|
| LiDAR–image alignment and labeling | LiDAR + images + GT for annotation | Code · Entry point | Software notes · BibTeX |
| LiDAR trajectory labeling tool | Point clouds + optional trajectory reference | Code · Entry point | Software notes · BibTeX |
| MMAUD synthetic data generation | Simulated RGB and LiDAR; recorded flight trajectory | Code · Entry point | Software notes · BibTeX |
| Sound Processing toolkit | Microphone-array audio | Code · Entry point | Software notes · BibTeX |
| EvoKF trajectory experiments | Trajectories with simulated measurement noise | Code · Entry point | Software notes · BibTeX |
Related implementations and tutorials
- ResNet / VisualNet, AudioNet and VorasNet: Code · Checked implementation. Additional comparison modules. VisualNet wraps pretrained ResNet-50, so it is grouped with ResNet rather than counted as a new architecture. The module needs a training/data pipeline. Software notes · BibTeX.
- AAUTE-style audio CNN: Code · Checked implementation. Community audio trajectory workflow with preprocessing and smoothing; use its own setup rather than treating it as the original AAUTE release. Software notes · BibTeX.
- AV-PedAware: Code and preprocessing tutorial · Paper.
Code, paper identities and bibliography mappings were checked on 26 October 2026. See the linked papers for experimental results.
Calibration and coordinate frames
🧭 Calibration overview · Transforms and ROS TF · Initial radar-to-camera matrix
The calibration collection includes official left/right fisheye YAML parameters, TXT/PDF reports, CAD drawings, individual STL parts and the complete fisheye calibration archive, including the original calibration bags. The cameras use the omni + radtan model at 1280 × 960.
Use the supplied calibration and CAD geometry as an initial calibration, then refine the inter-sensor transforms on your calibration data. The CAD-derived radar-to-left-camera matrix states its axis and origin assumptions; camera intrinsics, installation geometry and cross-sensor transforms are documented separately.
LiDAR, camera and Leica data share a common time reference while retaining their own spatial frames. V1 needs trajectory-based spatial alignment to Leica; V2 includes an added calibration recording. Use time-paired trajectories to estimate the spatial rotation and translation for V1. See the version-specific alignment guide for the workflow and V1 export-axis conventions.
Verified point/cluster annotations are not included. Ground-truth search regions can support candidate annotation generation; see Common Issues for the current annotation scope.
Verification and documentation
🔎 Every release file has a recorded size and SHA-256. All filesystem files were compared individually with public repository metadata. One sample from each of the 25 sequence/modality folders was also downloaded in full anonymously and hashed. The calibration ZIP passed a full-member CRC check.
| Resource | What it provides |
|---|---|
| SHA256SUMS.txt | Release-wide checksums, excluding the checksum file itself |
| RELEASE_VERIFICATION.json | Counts, byte totals, verification methods and completion record |
| Source coverage | Mapping from source files to public paths, with explicit exclusions |
| Filesystem checksums | Per-file hashes for all 76,786 extracted PNG/NPY files |
| Common Issues | 18 practical answers on downloads, timing, calibration, annotations and use |
| Complete issue index | 69 GitHub issues and linked discussions, reviewed on 23 October 2026 |
| Baseline guide | Code links, configurations and preparation steps |
| File layout | Directory structure, sequence counts and source coverage |
License and citation
The dataset is released under CC BY-NC-SA 4.0, following the original MMAUD dataset documentation.
📚 Please cite the ICRA 2024 paper when using MMAUD:
Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen, Jianfei Yang, Fen Liu, Jianping Li, Han Wang, and Lihua Xie. MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats. 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 2745–2751, 2024.
IEEE / DOI · arXiv · Paper PDF · Download BibTeX
@inproceedings{yuan2024MMAUD,
author = {Yuan, Shenghai and Yang, Yizhuo and Nguyen, Thien Hoang and Nguyen, Thien-Minh and Yang, Jianfei and Liu, Fen and Li, Jianping and Wang, Han and Xie, Lihua},
title = {{MMAUD}: A Comprehensive Multi-Modal Anti-{UAV} Dataset for Modern Miniature Drone Threats},
booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2024},
pages = {2745--2751},
doi = {10.1109/ICRA57147.2024.10610957}
}
Baseline references
Download the combined dataset and baseline BibTeX. The dataset citation comes first, followed by paper and software references.
| Method / resource | Paper / reference and BibTeX |
|---|---|
| ResNet | Paper · BibTeX |
| Darknet | Paper · BibTeX |
| VorasNet / DroneChase | Paper · BibTeX |
| AV-PED | Paper · BibTeX |
| Audionet | Paper · BibTeX |
| AV-FDTI | Paper · BibTeX |
| TAME | Paper · BibTeX |
| AAUTE | Paper · BibTeX |
| UnLiDAR | Paper · BibTeX |
| MMUDCT | Paper · BibTeX |
| AV-DTEC | Paper · BibTeX |
| LMMUAVC — lightweight fusion | Software DOI · BibTeX |
| TARF | Software notes · BibTeX |
| MMAUD acoustic Transformer | Software notes · BibTeX |
| YOLOv8 + geometry + Kalman tracking | Software notes · BibTeX |
| PointNeXt-S | Paper · BibTeX |
| YOLOv8 | Software notes · BibTeX |
| YOLOv10 | Paper · BibTeX |
| YOLO11 | Software notes · BibTeX |
| LiDAR–image alignment and labeling | Software notes · BibTeX |
| LiDAR trajectory labeling tool | Software notes · BibTeX |
| MMAUD synthetic data generation | Software notes · BibTeX |
| Sound Processing toolkit | Software notes · BibTeX |
| EvoKF trajectory experiments | Software notes · BibTeX |
| YOLOv5 | Software notes · BibTeX |
| ResNet / VisualNet, AudioNet and VorasNet | Software notes · BibTeX |
| AAUTE-style audio CNN | Software notes · BibTeX |
Baseline BibTeX entries
@inproceedings{he2016resnet,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
title = {Deep Residual Learning for Image Recognition},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016},
pages = {770--778},
doi = {10.1109/cvpr.2016.90},
url = {https://doi.org/10.1109/cvpr.2016.90}
}
@misc{redmon2018yolov3,
author = {Redmon, Joseph and Farhadi, Ali},
title = {{YOLOv3}: An Incremental Improvement},
year = {2018},
eprint = {1804.02767},
archiveprefix = {arXiv},
primaryclass = {cs.CV},
url = {https://arxiv.org/abs/1804.02767}
}
@inproceedings{vora2023dronechase,
author = {Vora, Neel and Wu, Yi and Liu, Jian and Nguyen, Phuc},
title = {{DroneChase}: A Mobile and Automated Cross-Modality System for Continuous Drone Tracking},
booktitle = {Proceedings of the Ninth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications},
year = {2023},
pages = {8--14},
doi = {10.1145/3597060.3597237},
url = {https://doi.org/10.1145/3597060.3597237}
}
@inproceedings{yang2023avpedaware,
author = {Yang, Yizhuo and Yuan, Shenghai and Cao, Muqing and Yang, Jianfei and Xie, Lihua},
title = {{AV-PedAware}: Self-Supervised Audio-Visual Fusion for Dynamic Pedestrian Awareness},
booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2023},
pages = {1871--1877},
doi = {10.1109/iros55552.2023.10342257},
url = {https://doi.org/10.1109/iros55552.2023.10342257}
}
@article{yang2024avfdti,
author = {Yang, Yizhuo and Yuan, Shenghai and Yang, Jianfei and Nguyen, Thien Hoang and Cao, Muqing and Nguyen, Thien-Minh and Wang, Han and Xie, Lihua},
title = {{AV-FDTI}: Audio-visual fusion for drone threat identification},
journal = {Journal of Automation and Intelligence},
year = {2024},
volume = {3},
number = {3},
pages = {144--151},
doi = {10.1016/j.jai.2024.06.002},
url = {https://doi.org/10.1016/j.jai.2024.06.002}
}
@inproceedings{xiao2025tame,
author = {Xiao, Zhenyuan and Hu, Huanran and Xu, Guili and He, Junwei},
title = {{TAME}: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification},
booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2025},
pages = {1--5},
doi = {10.1109/icassp49660.2025.10888776},
url = {https://doi.org/10.1109/icassp49660.2025.10888776}
}
@inproceedings{lei2025aaute,
author = {Lei, Allen H.-X. and Deng, Tianchen and Wang, Han and Yang, Jianfei and Yuan, Shenghai},
title = {Audio Array-Based {3D} {UAV} Trajectory Estimation with {LiDAR} Pseudo-Labeling},
booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2025},
pages = {1--5},
doi = {10.1109/icassp49660.2025.10887734},
url = {https://doi.org/10.1109/icassp49660.2025.10887734}
}
@inproceedings{liang2025unlidar,
author = {Liang, Hanfang and Yang, Yizhuo and Hu, Jinming and Yang, Jianfei and Liu, Fen and Yuan, Shenghai},
title = {Unsupervised {UAV} {3D} Trajectories Estimation with Sparse Point Clouds},
booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2025},
pages = {1--5},
doi = {10.1109/icassp49660.2025.10890359},
url = {https://doi.org/10.1109/icassp49660.2025.10890359}
}
@misc{deng2024mmudct,
author = {Deng, Tianchen and Zhou, Yi and Wu, Wenhua and Li, Mingrui and Huang, Jingwei and Liu, Shuhong and Song, Yanzeng and Zuo, Hao and Wang, Yanbo and Yue, Yutao and Wang, Hesheng and Chen, Weidong},
title = {Multi-Modal {UAV} Detection, Classification and Tracking Algorithm -- Technical Report for {CVPR} 2024 {UG2} Challenge},
year = {2024},
eprint = {2405.16464},
archiveprefix = {arXiv},
primaryclass = {cs.RO},
url = {https://arxiv.org/abs/2405.16464}
}
@article{xiao2026avdtec,
author = {Xiao, Zhenyuan and Yuan, Shenghai and Xu, Guili and Zeng, Xianglong and Hu, Huanran and He, Junwei and Yang, Yizhuo},
title = {{AV-DTEC}: Self-Supervised Audio-Visual Fusion for Drone {3-D} Trajectory Estimation and Classification},
journal = {IEEE Sensors Journal},
year = {2026},
volume = {26},
number = {10},
pages = {15912--15924},
doi = {10.1109/jsen.2026.3680898},
url = {https://doi.org/10.1109/jsen.2026.3680898}
}
@misc{code_LMMUAVC_lightweight_fusion,
author = {Feng, Yibo and Li, Ning and Wu, Di and Ma, Ximing and Yu, Xiangyue and Bao, Haiyang},
title = {Code for: Lightweight multimodal fusion for UAV classification with explicit missing-modality modeling},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.22670375},
url = {https://doi.org/10.5281/zenodo.22670375},
note = {Software, version 1.0.0}
}
@misc{code_TARF,
key = {TARF},
title = {TARF: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/hasiburrahman875/tarf_mmuav},
note = {Accessed 2026-10-26; revision 4621a17ae9feaabe9f21ea0337e7b62d6bc6b49a}
}
@misc{code_MMAUD_acoustic_Transformer,
key = {MMAUD acoustic Transformer},
title = {MMAUD acoustic Transformer: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/Shubham-3256/Baseline-MMAUD-acoustic-transformer-implementation-p2},
note = {Accessed 2026-10-26; revision 5283b488757cf5cca531e959c074f99ed26caa4e}
}
@misc{code_YOLOv8_geometry_Kalman_tracking,
key = {YOLOv8 + geometry + Kalman tracking},
title = {YOLOv8 + geometry + Kalman tracking: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/sebovelan/drone-multimodal-detection},
note = {Accessed 2026-10-26; revision 3926248da1c473822d19cd15bed2055f99964c0e}
}
@inproceedings{qian2022pointnext,
title = {PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies},
author = {Qian, Guocheng and Li, Yuchen and Peng, Houwen and Mai, Jinjie and Hammoud, Hasan and Elhoseiny, Mohamed and Ghanem, Bernard},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2022},
url = {https://arxiv.org/abs/2206.04670}
}
@misc{code_YOLOv8,
key = {YOLOv8},
title = {YOLOv8: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/ultralytics/ultralytics},
note = {Accessed 2026-10-26; revision aa1f778a75a610f7dd2aa4762df1c25f6a3063d7}
}
@inproceedings{wang2024yolov10,
title = {YOLOv10: Real-Time End-to-End Object Detection},
author = {Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
year = {2024},
booktitle = {Advances in Neural Information Processing Systems},
volume = {37},
url = {https://arxiv.org/abs/2405.14458}
}
@misc{code_YOLO11,
key = {YOLO11},
title = {YOLO11: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/ultralytics/ultralytics},
note = {Accessed 2026-10-26; revision aa1f778a75a610f7dd2aa4762df1c25f6a3063d7}
}
@misc{code_LiDAR_image_alignment_and_labeling,
key = {LiDAR–image alignment and labeling},
title = {LiDAR–image alignment and labeling: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/Blank-Paradox/LiDAR-and-visible-light-MMAUD-dataset},
note = {Accessed 2026-10-26; revision fd214ab28c629ba20cf8b906c7a4bd07cd3c695e}
}
@misc{code_LiDAR_trajectory_labeling_tool,
key = {LiDAR trajectory labeling tool},
title = {LiDAR trajectory labeling tool: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/Hsief/lidar-trajectory-label-tool},
note = {Accessed 2026-10-26; revision 8715ede3ccff1f51b4fc3542580f305d8a61f1d5}
}
@misc{code_MMAUD_synthetic_data_generation,
key = {MMAUD synthetic data generation},
title = {MMAUD synthetic data generation: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/C-nwokoye/MMAUD_Research},
note = {Accessed 2026-10-26; revision 3fd9e714293c28a0fb91c02cded785615ec335bf}
}
@misc{code_Sound_Processing_toolkit,
key = {Sound Processing toolkit},
title = {Sound Processing toolkit: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/vb-pogribnyi/sound_processing},
note = {Accessed 2026-10-26; revision 4719cacd9d197addbc21dafe49fb3518606026c5}
}
@misc{code_EvoKF_trajectory_experiments,
key = {EvoKF trajectory experiments},
title = {EvoKF trajectory experiments: public implementation},
howpublished = {GitHub repository},
url = {https://github.com/mihaozbot/Evolving-drone-tracking},
note = {Accessed 2026-10-26; revision 83b525614a6a90a78ff14b1f6d04eef63694906b}
}
@misc{code_YOLOv5,
key = {YOLOv5},
title = {YOLOv5: public code and dataset resources},
howpublished = {GitHub repository},
url = {https://github.com/ultralytics/yolov5},
note = {Accessed 2026-10-26}
}
@misc{code_ResNet_VisualNet_AudioNet_and_VorasNet,
key = {ResNet / VisualNet, AudioNet and VorasNet},
title = {ResNet / VisualNet, AudioNet and VorasNet: public code and dataset resources},
howpublished = {GitHub repository},
url = {https://github.com/hxlei0827/UAV_comparison},
note = {Accessed 2026-10-26}
}
@misc{code_AAUTE_style_audio_CNN,
key = {AAUTE-style audio CNN},
title = {AAUTE-style audio CNN: public code and dataset resources},
howpublished = {GitHub repository},
url = {https://github.com/Shubham-3256/audio-uav-trajectory-p1-},
note = {Accessed 2026-10-26}
}
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Papers for Yuan-Shenghai/MMAUD
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