UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles

• URL: http://arxiv.org/abs/2410.11125v2
• Date: Thu, 17 Oct 2024 02:34:39 GMT
• Title: UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles
• Authors: Hui Ye, Rajshekhar Sunderraman, Shihao Ji,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) are employed in numerous applications, including aerial photography, surveillance, and agriculture. Existing benchmarks for UAV applications are mainly designed for traditional 2D perception tasks. UAV3D comprises 1,000 scenes, each of which has 20 frames with fully annotated 3D bounding boxes on vehicles.
• Score: 12.278437831053985
• License:
• Abstract: Unmanned Aerial Vehicles (UAVs), equipped with cameras, are employed in numerous applications, including aerial photography, surveillance, and agriculture. In these applications, robust object detection and tracking are essential for the effective deployment of UAVs. However, existing benchmarks for UAV applications are mainly designed for traditional 2D perception tasks, restricting the development of real-world applications that require a 3D understanding of the environment. Furthermore, despite recent advancements in single-UAV perception, limited views of a single UAV platform significantly constrain its perception capabilities over long distances or in occluded areas. To address these challenges, we introduce UAV3D, a benchmark designed to advance research in both 3D and collaborative 3D perception tasks with UAVs. UAV3D comprises 1,000 scenes, each of which has 20 frames with fully annotated 3D bounding boxes on vehicles. We provide the benchmark for four 3D perception tasks: single-UAV 3D object detection, single-UAV object tracking, collaborative-UAV 3D object detection, and collaborative-UAV object tracking. Our dataset and code are available at https://huiyegit.github.io/UAV3D_Benchmark/.

Related papers

• UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection [0.03464344220266879]
  Patch Intensity Convergence (PIC) technique generates high-fidelity bounding boxes for UAV detection without manual labeling. This technique forms the foundation of UAVDB, a dedicated database designed specifically for UAV detection. We benchmark UAVDB using state-of-the-art (SOTA) YOLO series detectors, providing a comprehensive performance analysis.
  arXiv  Detail & Related papers  (2024-09-09T13:27:53Z)
• UCDNet: Multi-UAV Collaborative 3D Object Detection Network by Reliable Feature Mapping [14.401624713578737]
  Multi-UAV collaborative 3D object detection can perceive and comprehend complex environments. We propose an unparalleled camera-based multi-UAV collaborative 3D object detection paradigm called UCDNet. We show our method increases 4.7% and 10% mAP respectively compared to the baseline.
  arXiv  Detail & Related papers  (2024-06-07T05:27:32Z)
• 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)
• Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis [54.07535860237662]
  We propose a novel task for learned UAV video coding and construct a comprehensive and systematic benchmark for such a task. It is expected that the benchmark will accelerate the research and development in video coding on drone platforms.
  arXiv  Detail & Related papers  (2023-01-15T15:18:02Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking [40.83427270297245]
  WebUAV-3M is a million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark. We collect 4,485 videos with more than 3M frames from the Internet. WebUAV-3M is by far the largest public UAV tracking benchmark.
  arXiv  Detail & Related papers  (2022-01-19T05:39:42Z)
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification [21.48667873335246]
  Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. The coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras. We propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area.
  arXiv  Detail & Related papers  (2021-04-13T14:13:09Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• 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.