In most modern object detection pipelines, the detection proposals are processed independently given the feature map. Therefore, they overlook the underlying relationships between objects and the surrounding background, which could have provided additional context for accurate detection. Because aerial imagery is almost orthographic, the spatial relations in image space closely align with those in the physical world, and inter-object and object-background relationships become particularly significant. To address this oversight, we propose a framework that leverages the strengths of Transformer-based models and Contrastive Language-Image Pre-training (CLIP) features to capture such relationships. Specifically, Building on two-stage detectors, we treat Region of Interest (RoI) proposals as tokens, accompanied by CLIP Tokens obtained from multi-level image segments. These tokens are then passed through a Transformer encoder, where specific spatial and geometric relations are incorporated into the attention weights, which are adaptively modulated and regularized. Additionally, we introduce self-supervised constraints on CLIP Tokens to ensure consistency. Extensive experiments on three benchmark datasets demonstrate that our approach achieves consistent improvements, setting new state-of-the-art results with increases of 1.37 mAP$_{50}$ on DOTA-v1.0, 5.30 mAP$_{50}$ on DOTA-v1.5, 2.30 mAP$_{50}$ on DOTA-v2.0 and 3.23 mAP$_{50}$ on DIOR-R.
Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detect…
Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance in many aerial vision-based applications. Despite the great success of generic object detection methods, a significant performance drop is observed when applied to images captured by UAVs. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and different capture times. These variations lead to domain imbalances and, thus, trained models suffering from domain bias. We demonstrate that domain knowledge is a valuable source of information and thus propose…
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；University of Missouri（密苏里大学）；Computer Science University of Missouri Columbia, MO, USA（计算机科学密苏里大学哥伦比亚分校）
Object Detection in Aerial Images in Scarce Data Regimes
Université Sorbonne Paris Nord（索邦巴黎北大学）；COSE；CentraleSupélec（巴黎中央理工-高等电力学院）；Université de Rouen Normandie（鲁昂诺曼底大学）；École Centrale de Lyon（里昂中央理工学院）；ETS Montréal（蒙特利尔工程学院）
Prince Sultan University（苏丹王子大学）；Benha University（本哈大学）
Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traff…
SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology（电子信息与通信学院，华中科技大学）；Baidu Inc., China（百度公司，中国）；School of Software Engineering, Huazhong University of Science and Technology（软件工程学院，华中科技大学）
CLIP-Guided Source-Free Object Detection in Aerial Images
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；Institute for Infocomm Research (I2R), A-STAR（新加坡科技研究局信息通信研究院）
Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images
Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India（电子与通信工程系，Manipal理工学院，Manipal高等教育学院，Manipal，印度）
Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；City University of Hong Kong（香港城市大学）
Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy is limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these pro…
Deep learning is revolutionizing the mapping industry. Under lightweight human curation, computer has generated almost half of the roads in Thailand on OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are displaying 125 million computer-generated building polygons in the U.S. While tremendously more efficient than manual mapping, one cannot map out everything from the air. Especially for roads, a small prediction gap by image occlusion renders the entire road useless for routing. Misconnections can be more dangerous. Therefore computer-based mapping often requires local veri…
A Novel Compression Framework for YOLOv8: Achieving Real-Time Aerial Object Detection on Edge Devices via Structured Pruning and Channel-Wise Distillation
Malek Ashtar University of Technology（马利克·阿什塔尔理工大学）
SFFR: Spatial-Frequency Feature Reconstruction for Multispectral Aerial Object Detection
School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；School of Electrical and Information Engineering, Jiangsu University（江苏大学电气与信息工程学院）；School of Automation, Southeast University（东南大学自动化学院）
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently. In this paper, we revisit this problem and point out the ignored issue about image alignment information. We show that the performance of a simple Siamese network is highly dependent on the alignment setting and the comparison of previous works can be unfair if they have different assumptions. Instead of focusing on the feature extraction under the alignment assumption, we show that improvements in metric learning tech…
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；University of British Columbia (UBC)（不列颠哥伦比亚大学）
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time- and labor-consuming. To address this,…
UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking
Silicon Orchard Research and Analytics Lab（Silicon Orchard 研究与分析实验室）；Daffodil International University（水仙花国际大学）；BRAC University（BRAC 大学）；Texas Tech University（德克萨斯理工大学）；Clark Atlanta University（克拉克亚特兰大大学）
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）；Cardiff University（卡迪夫大学）；Beihang University（北航）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东大数据分析与处理重点实验室）
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；University of Otago（奥塔哥大学）；Princeton University（普林斯顿大学）；Marine Bycatch and Threats – Department of Conservation（海洋误捕与威胁——保护部门）；Antarctic Research Trust（南极研究信托）；The Tawaki Trust（塔瓦基信托）；Global Penguin Society（全球企鹅协会）
Northern Arizona University（北亚利桑那大学）；Air Force Research Laboratory（美国空军研究实验室）
Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management …
