Existing methods for object detection in UAV images ignored an important challenge - imbalanced class distribution in UAV images - which leads to poor performance on tail classes. We systematically investigate existing solutions to long-tail problems and unveil that re-balancing methods that are effective on natural image datasets cannot be trivially applied to UAV datasets. To this end, we rethink long-tailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images. The key components in DSHNet include Class-Biased Samplers (CBS) and Bilateral Box Heads (BBH), which are developed to cope with tail classes and head classes in a dual-path manner. Without bells and whistles, DSHNet significantly boosts the performance of tail classes on different detection frameworks. Moreover, DSHNet significantly outperforms base detectors and generic approaches for long-tail problems on VisDrone and UAVDT datasets. It achieves new state-of-the-art performance when combining with image cropping methods. Code is available at https://github.com/we1pingyu/DSHNet
AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes
College of Computer and Data Science（计算机与数据科学学院）；Engineering Research Center of Big Data Intelligence, Ministry of Education（大数据智能工程研究中心）；Key Laboratory of Intelligent Metro of Universities in Fujian（福建省高校智能地铁重点实验室）；School of Software and Information Engineering（软件与信息工程学院）
School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
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）
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large dataset…
Drones or UAVs, equipped with different sensors, have been deployed in many places especially for urban traffic monitoring or last-mile delivery. It provides the ability to control the different aspects of traffic given real-time obeservations, an important pillar for the future of transportation and smart cities. With the increasing use of such machines, many previous state-of-the-art object detectors, who have achieved high performance on front facing cameras, are being used on UAV datasets. When applied to high-resolution aerial images captured from such datasets, they fail to generalize to…
An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performance of the object detector. Many works nowadays focus either on improving the image quality or improving the object detection models independently, but neglect the importance of joint optimization of the two subsystems. The goal of this paper is to tune the detection throughput and accuracy of existing object detectors in the remote sensing scenario by focusing on optimizing the input images tailored to the object det…
High-Frequency Semantics and Geometric Priors for End-to-End Detection Transformers in Challenging UAV Imagery
College of Mathematics and Informatics（数学与信息学院）；South China Agricultural University（华南农业大学）；Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs（热带南中国智能农业技术重点实验室，农业农村部）；School of Economics and Trade（经济贸易学院）
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios
Center for Ubiquitous Computing (UBICOMP), University of Oulu, Finland（通用计算中心（UBICOMP），奥卢大学，芬兰）；Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland（机器视觉与信号分析中心（CMVS），奥卢大学，芬兰）；Center for Wireless Communications (CWC), University of Oulu, Finland（无线通信中心（CWC），奥卢大学，芬兰）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, United States（明斯希德电气与计算机工程系，南加州大学，美国）；Department of Computer Science, University of Helsinki, Finland（计算机科学系，赫尔辛基大学，芬兰）；University of Oulu, Oulu, Finland（奥卢大学，奥卢，芬兰）；University of Helsinki, Helsinki, Finland（赫尔辛基大学，赫尔辛基，芬兰）；University of Southern California, Los Angeles, United States（南加州大学，洛杉矶，美国）
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；City University of Hong Kong（香港城市大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors
South China University of Technology（华南理工大学）；Bytedance（字节跳动）；Tsinghua University（清华大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；Guangming Laboratory（光明实验室）
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining
Department of Electronic and Communication Engineering, North China Electric Power University（电子与通信工程系，华北电力大学）；Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University（河北省电力物联网技术重点实验室，华北电力大学）；Computer Science and Software Engineering Department, Monmouth University（计算机科学与软件工程系，蒙特莫恩大学）
SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet
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）；University of Bonn（波恩大学）；Institute for Sugar Beet Research (IfZ)（甜菜研究所）
Institute for Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形与视觉研究所）；OMICRON electronics GmbH（奥米克龙电子有限公司）
In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without …
Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization
School of Automation, Southeast University（东南大学自动化学院）；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）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；School of Computer Science, Southeast University（东南大学计算机科学学院）
DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images
National College for Excellent Engineers, Beihang University, Beijing, China（北京航空航天大学优秀工程师学院）；School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）；School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China（合肥工业大学计算机科学与信息工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室）；Innovation Center for Intelligent System Cognition and Decision-Making, Beijing, China（智能系统认知与决策创新中心）；Information Science Academy of China Electronics Technology Group Corporation, Beijing, China（中国电子科技集团有限公司信息科学研究院）
