无人机（UAV）：UAV数据集在分割、分类、检测与跟踪中的多样化应用
UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking
AI总结：
本文系统综述了UAV数据集在分割、分类、检测与跟踪等任务中的多样化应用，强调其对推动计算机视觉模型发展和多领域实际创新的关键作用。
摘要：
无人机（Unmanned Aerial Vehicles, UAVs）极大地革新了在多种研究领域中收集和分析数据的过程，提供了无与伦比的适应性和有效性。本文对无人机（Unmanned Aerial Vehicle, UAV）数据集进行了全面考察，重点强调其广泛的应用范围和进展。UAV数据集包含多种类型的数据，例如卫星影像、无人机拍摄的图像以及视频。这些数据集可以被归类为单模态或多模态，提供广泛而详细且全面的信息。这些数据集在灾害损失评估、空中监视、目标识别和跟踪中发挥着关键作用。它们促进了用于语义分割、姿态估计、车辆重识别和手势识别等任务的复杂模型的开发。通过利用UAV数据集，研究人员能够显著增强计算机视觉模型的能力，从而推动技术进步，并提升我们从空中视角理解复杂动态环境的能力。本综述旨在概括UAV数据集的多方面效用，强调它们在推动多个领域的创新和实际应用中的关键作用。
英文摘要：
Unmanned Aerial Vehicles (UAVs), have greatly revolutionized the process of gathering and analyzing data in diverse research domains, providing unmatched adaptability and effectiveness. This paper presents a thorough examination of Unmanned Aerial Vehicle (UAV) datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
