MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature
Drone Threats
- URL: http://arxiv.org/abs/2402.03706v1
- Date: Tue, 6 Feb 2024 04:57:07 GMT
- Title: MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature
Drone Threats
- Authors: Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen,
Jianfei Yang, Fen Liu, Jianping Li, Han Wang, Lihua Xie
- Abstract summary: MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation.
It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB.
Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools.
- Score: 37.981623262267036
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In response to the evolving challenges posed by small unmanned aerial
vehicles (UAVs), which possess the potential to transport harmful payloads or
independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal
Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat
detection methodologies by focusing on drone detection, UAV-type
classification, and trajectory estimation. MMAUD stands out by combining
diverse sensory inputs, including stereo vision, various Lidars, Radars, and
audio arrays. It offers a unique overhead aerial detection vital for addressing
real-world scenarios with higher fidelity than datasets captured on specific
vantage points using thermal and RGB. Additionally, MMAUD provides accurate
Leica-generated ground truth data, enhancing credibility and enabling confident
refinement of algorithms and models, which has never been seen in other
datasets. Most existing works do not disclose their datasets, making MMAUD an
invaluable resource for developing accurate and efficient solutions. Our
proposed modalities are cost-effective and highly adaptable, allowing users to
experiment and implement new UAV threat detection tools. Our dataset closely
simulates real-world scenarios by incorporating ambient heavy machinery sounds.
This approach enhances the dataset's applicability, capturing the exact
challenges faced during proximate vehicular operations. It is expected that
MMAUD can play a pivotal role in advancing UAV threat detection,
classification, trajectory estimation capabilities, and beyond. Our dataset,
codes, and designs will be available in https://github.com/ntu-aris/MMAUD.
Related papers
- Robust Low-Cost Drone Detection and Classification in Low SNR Environments [0.9087641068861043]
We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones.
We demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna.
arXiv Detail & Related papers (2024-06-26T12:50:55Z) - Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge [20.459377705070043]
This report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge.
We propose a multi-modal UAV detection, classification, and 3D tracking method for accurate UAV classification and tracking.
Our system integrates cutting-edge classification techniques and sophisticated post-processing steps to boost accuracy and robustness.
arXiv Detail & Related papers (2024-05-26T07:21:18Z) - Evidential Detection and Tracking Collaboration: New Problem, Benchmark
and Algorithm for Robust Anti-UAV System [56.51247807483176]
Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military.
Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided.
In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
arXiv Detail & Related papers (2023-06-27T19:30:23Z) - Archangel: A Hybrid UAV-based Human Detection Benchmark with Position
and Pose Metadata [10.426019628829204]
Archangel is the first UAV-based object detection dataset composed of real and synthetic subsets.
A series of experiments are carefully designed with a state-of-the-art object detector to demonstrate the benefits of leveraging the metadata.
arXiv Detail & Related papers (2022-08-31T21:45:16Z) - CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of
Adversarial Robustness of Vision Models [61.68061613161187]
This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches.
The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving.
The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
arXiv Detail & Related papers (2022-06-09T09:17:38Z) - Vision-based Anti-UAV Detection and Tracking [18.307952561941942]
Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern.
We propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV.
It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences.
arXiv Detail & Related papers (2022-05-22T15:21:45Z) - Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments [20.69412701553767]
Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning.
In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs.
This paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task.
arXiv Detail & Related papers (2022-01-23T07:18:55Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation.
We consider the task of tracking UAVs, providing rich information such as location and trajectory.
We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
arXiv Detail & Related papers (2021-01-21T07:00:15Z) - Perceiving Traffic from Aerial Images [86.994032967469]
We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images.
We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
arXiv Detail & Related papers (2020-09-16T11:37:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.