This review provides a detailed analysis of the advancements in unmanned aerial vehicle (UAV) detection and classification systems from 2020 to today. It covers various detection methodologies such as radar, radio frequency, optical, and acoustic sensors, and emphasizes their integration via sophisticated sensor fusion techniques. The fundamental technologies driving UAV detection and classification are thoroughly examined, with a focus on their accuracy and range. Additionally, the paper discusses the latest innovations in artificial intelligence and machine learning, illustrating their impact on improving the accuracy and efficiency of these systems. The review concludes by predicting further technological developments in UAV detection, which are expected to enhance both performance and reliability.
Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Modern Control Technology Research Institute（西安现代控制技术研究所）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
精准农业被认为是追求低投入、高效率和可持续农业的基本途径。为了实现这一目标，需要对当地作物状况进行可靠和最新的描述。遥感，特别是卫星图像，已证明是作物制图、监测和疾病评估的宝贵工具。然而，免费提供的低分辨率或中等分辨率的卫星图像在具体的农业应用方面显示出一些限制，例如，农作物是一排排种植的。事实上，在这个框架内，卫星的输出可能会受到行内覆盖的影响，从而提供关于作物状况的不准确信息。本文提出了一种新的卫星图像细化框架，基于深度学习技术，该技术充分利用了无人机（UAV）机载多光谱传感器获取的高分辨率图像中的信息。为了训练卷积神经网络，仅需要单个无人机驱动的数据集，使得所提出的方法简单且具有成本效益。Serralunga d 'Alba（意大利北部）的葡萄园被选为验证目的的案例研究。细化卫星驱动的归一化差异植被指数（NDVI）地图，在葡萄生长季节的四个不同时期获得的，被证明是更好地描述作物状况相对于原始数据集的相关性分析和方差分析。此外，使用基于K-均值的分类器，3级葡萄园活力地图是有益的，从NDVI地图，这是一个有价值的工具，为种植者。
Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges
College of Computer Science and Engineering, Guilin University of Technology, China（桂林理工大学计算机科学与工程学院）；Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, China（广西嵌入式技术与智能系统重点实验室）；JD Logistic, Beijing, China（京东物流）
Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Optics and Electronics（光电技术研究所）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Wuhan University of Technology（武汉理工大学）；Chongqing Research Institute, Wuhan University of Technology（武汉理工大学重庆研究院）；Nanyang Technological University（南洋理工大学）
OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Tracking and Telecommunication Technology（北京跟踪与通信技术研究所）
Recent Advances in Transformer and Large Language Models for UAV Applications
LSEA Laboratory, Department of Electrical Engineering, University of Medea, 26000, Algeria（里塞阿实验室，电气工程系，梅迪亚大学，阿尔及利亚）；Institute of Technology, University Centre Salhi Ahmed, Naama, Algeria（技术研究所，萨利哈·阿赫迈德大学中心，纳马，阿尔及利亚）；Cybersecurity Institute, Department of Computer Science, University of Liverpool, L69 3BX, Liverpool, UK（网络安全研究所，计算机科学系，利物浦大学，英国）；LCPTS Laboratory, University of Sciences（LCPTS实验室，科学大学）；College of Computing（计算学院）；Data Science, Nanyang Technological University, Singapore.（数据科学，南洋理工大学，新加坡）
