Compared with existing vehicle re-identification (ReID) tasks conducted with datasets collected by fixed surveillance cameras, vehicle ReID for unmanned aerial vehicle (UAV) is still under-explored and could be more challenging. Vehicles with the same color and type show extremely similar appearance from the UAV's perspective so that mining fine-grained characteristics becomes necessary. Recent works tend to extract distinguishing information by regional features and component features. The former requires input images to be aligned and the latter entails detailed annotations, both of which are difficult to meet in UAV application. In order to extract efficient fine-grained features and avoid tedious annotating work, this letter develops an unsupervised self-aligned network consisting of three branches. The network introduced a self-alignment module to convert the input images with variable orientations to a uniform orientation, which is implemented under the constraint of triple loss function designed with spatial features. On this basis, spatial features, obtained by vertical and horizontal segmentation methods, and global features are integrated to improve the representation ability in embedded space. Extensive experiments are conducted on UAV-VeID dataset, and our method achieves the best performance compared with recent ReID works.
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所（IAIS））；University of Bonn（波恩大学）；Institute for Sugar Beet Research (IfZ)（甜菜研究所（IfZ））；Technical University of Munich（慕尼黑工业大学）
UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated…
Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials. Field experiments, which may comprise thousands of plants, are planted according to experimental designs to evaluate varieties or management practices. For many types of phenotyping analysis, we examine smaller groups of plants known as "plots." In this paper, we propose a new plot extraction method that will segment a UAV image into plots. We will demonstrate that our method achieves higher plo…
Seoul National University（首尔国立大学）；Microsoft Research（微软研究院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）
We propose a novel network that learns a part-aligned representation for person re-identification. It handles the body part misalignment problem, that is, body parts are misaligned across human detections due to pose/viewpoint change and unreliable detection. Our model consists of a two-stream network (one stream for appearance map extraction and the other one for body part map extraction) and a bilinear-pooling layer that generates and spatially pools a part-aligned map. Each local feature of the part-aligned map is obtained by a bilinear mapping of the corresponding local appearance and body…
Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades are susceptible to both operational and weather-based damage over time, reducing the energy efficiency output of turbines. In this study, we address automating the otherwise time-consuming task of both blade detection and extraction, together with fault detection within UAV-captured turbine blade inspection imagery. We propose BladeNet, an applicati…
University at Buffalo（布法罗大学）；Clemson University（克莱姆森大学）
This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV). Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. Subsequently, a 2D planar lidar is used to allow conversion of pixel data to actual distance measurements, and thereby enable localization of the UAV in global coordinates. The monocular data is processed through a deep learning-based object detection method that computes an initial bounding box for the UAV. The thermal…
Cross-modal Offset-guided Dynamic Alignment and Fusion for Weakly Aligned UAV Object Detection
State Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences（光学场操控科学与技术国家重点实验室，中国科学院）；Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电研究所）
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
University of Peradeniya（佩拉德尼亚大学）；Multidisciplinary AI Research Centre, University of Peradeniya（佩拉德尼亚大学多学科人工智能研究中心）；Vega Innovations（维加创新公司）；Queensland University of Technology (QUT)（昆士兰科技大学）
Automating grapevine LAI features estimation with UAV imagery and machine learning
TeCIP Institute（TeCIP研究所）；Biorobotics Institute（生物机器人研究所）；Department of Agriculture Food and Environment（农业食品与环境系）；Università di Pisa（比萨大学）；Institute of Crop Science（作物科学研究所）
CISTER - Research Centre in Real-Time and Embedded Computing Systems（CISTER实时与嵌入式计算系统研究中心）；The Hong Kong Polytechnic University（香港理工大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（联邦科学与工业研究组织（CSIRO））；University of Manitoba（曼尼托巴大学）
The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operati…
Malaviya National Institute of Technology Jaipur（斋浦尔马拉维亚国家理工学院）
Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR)? The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, even…
University of Twente（特文特大学）；Wuhan University（武汉大学）
Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, wh…
ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
University of Twente（特文特大学）；Wuhan University（武汉大学）；Ohio State University（俄亥俄州立大学）
Semantic segmentation has been one of the leading research interests in computer vision recently. It serves as a perception foundation for many fields, such as robotics and autonomous driving. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the dri…
UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmark
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China（中国科学院计算机网络信息中心，北京100190，中国）；University of Chinese Academy of Sciences, Beijing 100190, China（中国科学院大学，北京100190，中国）；Anhui Provincial Key Laboratory of Multi-modal Cognitive Computation, Anhui University, Hefei 230601, China（安徽省多模态认知计算重点实验室，安徽大学，合肥230601，中国）；Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China（安徽省信息材料与智能感知实验室，安徽大学，合肥230601，中国）
Although much significant progress has been made in the research field of object detection with deep learning, there still exists a challenging task for the objects with small size, which is notably pronounced in UAV-captured images. Addressing these issues, it is a critical need to explore the feature extraction methods that can extract more sufficient feature information of small objects. In this paper, we propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible, improving the information propagation…
School of Robotics, Hunan University（湖南大学机器人学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Technical University Munich（慕尼黑工业大学）
Unmanned System Application Technology Research Institute, China Jiliang University（中国计量大学无人系统应用技术研究院）
Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the position shift and the uncertainty of distance and scale. Existing methods are mainly aimed at digging for more comprehensive fine-grained information. However, it underestimates the importance of extracting robust feature representation and the impact of feature alignment. The CNN-based methods have achieved great success in cross-view geo-localization. However it still has some limitations, e.g., it can only…
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance
Wuhan University of Technology（武汉理工大学）；University of Aberdeen（阿伯丁大学）；Guangdong University of Technology（广东工业大学）；The Hong Kong Polytechnic University（香港理工大学）
UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection
College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
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）
EgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning
Beihang University（北京航空航天大学）；Zhongguancun Academy（中关村学院）；Northeastern University（东北大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
Texas A&M University（德州农工大学）；Kwai Inc.（快手公司）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains. UAV's limited battery capacity motivates us to develop an energy-efficient video text spotting solution. In this paper, we first revisit RCNN's crop & resize training strategy and empirically find that it outperforms aligned RoI sampling on a real-world video text dataset captured by UAV. To reduce energy consumption, we further propose a multi-stage image processor that takes videos' redundancy, continuity, and mixed degradation into account. Lastly, the model is pruned and quant…
University of Southern California（南加州大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；China University of Mining and Technology（中国矿业大学）；Ubisoft（育碧）
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided additional high-resolution imaging capabilities with low cost for mapping a relatively large area of interest. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations. Our previous works have demonstrated the importance of information extraction from the derived photogram…
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；School of Electronic and Information Engineering, Foshan University（电子与信息工程学院，佛山大学）
UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks
College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；College of Artificial Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
EFSI-DETR: Efficient Frequency-Semantic Integration for Real-Time Small Object Detection in UAV Imagery
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（信息工程测绘遥感国家重点实验室）；Wuhan University（武汉大学）；Wuhan University Shenzhen Research Institute（武汉大学深圳研究院）；School of Computer Science（计算机学院）；School of Automation Science and Engineering（自动化科学与工程学院）；South China University of Technology（华南理工大学）
NeuroVoxel-LM: Language-Aligned 3D Perception via Dynamic Voxelization and Meta-Embedding
School of Electrical and Electronic Engineering（电气与电子工程学院）；Nanyang Technological University（南洋理工大学）；School of Computer Science and Technology（计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）；Dataminr（Dataminr公司）
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available re…
Scale-adaptive UAV Geo-localization via Height-aware Partition Learning
School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute of Collaborative Innovation, University of Macau（澳门大学协同创新研究院）
Classification of grapevine varieties using UAV hyperspectral imaging
Department of Computer Science（计算机科学系）；Department of Software Engineering（软件工程系）；Centre for Robotics in Industry（工业机器人中心）；University of Trás-os-Montes e Alto Douro（特拉斯-奥斯-蒙特斯和上杜罗大学）
To address the challenges in UAV object detection, such as complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions,this paper proposes PT-DETR based on RT-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. In the backbone network, we introduce the Partially-Aware Detail Focus (PADF) Module to enhance feature extraction for small objects. Additionally,we design the Median-Frequency Feature Fusion (MFFF) module,which effectively improves the model's ability to capture small-object details and contextual information. Furth…
