D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统重点实验室）；Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；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）
Zixiao Wen, Zhen Yang, Xianjie Bao, Lei Zhang, Xiantai Xiang, Wenshuai Li, Yuhan Liu
备注：
This work has been submitted to the IEEE for possible publication
Detecting tiny objects plays a vital role in remote sensing intelligent interpretation, as these objects often carry critical information for downstream applications. However, due to the extremely limited pixel information and significant variations in object density, mainstream Transformer-based detectors often suffer from slow convergence and inaccurate query-object matching. To address these challenges, we propose D$^3$R-DETR, a novel DETR-based detector with Dual-Domain Density Refinement. By fusing spatial and frequency domain information, our method refines low-level feature maps and utilizes their rich details to predict more accurate object density map, thereby guiding the model to precisely localize tiny objects. Extensive experiments on the AI-TOD-v2 dataset demonstrate that D$^3$R-DETR outperforms existing state-of-the-art detectors for tiny object detection.
UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
Easy Lab, College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China（易实验室，数学与信息学院，华南农业大学，广州，中国）；School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院，东南大学，南京，中国）；School of Mathematics, Foshan University, Foshan, China（数学学院，佛山大学，佛山，中国）
Scale-Aware Relay and Scale-Adaptive Loss for Tiny Object Detection in Aerial Images
School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Optics and Photonics, Beijing Institute of Technology（光学与 photonics 学院，北京理工大学）
ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
School of Electronics and Information Engineering, Harbin Institute of Technology（哈尔滨工业大学电子与信息工程学院）；OpenGVLab, Shanghai AI Laboratory（上海人工智能实验室OpenGVLab）
Despite the promising results, existing oriented object detection methods usually involve heuristically designed rules, e.g., RRoI generation, rotated NMS. In this paper, we propose an end-to-end framework for oriented object detection, which simplifies the model pipeline and obtains superior performance. Our framework is based on DETR, with the box regression head replaced with a points prediction head. The learning of points is more flexible, and the distribution of points can reflect the angle and size of the target rotated box. We further propose to decouple the query features into classif…
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Box-supervised instance segmentation has recently attracted lots of research efforts while little attention is received in aerial image domain. In contrast to the general object collections, aerial objects have large intra-class variances and inter-class similarity with complex background. Moreover, there are many tiny objects in the high-resolution satellite images. This makes the recent pairwise affinity modeling method inevitably to involve the noisy supervision with the inferior results. To tackle these problems, we propose a novel aerial instance segmentation approach, which drives the ne…
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）
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）
University of Amsterdam（阿姆斯特丹大学）；The University of Tokyo（东京大学）
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution of the objects, MPEG artifacts due to compression and a large searching area with many hard negatives. Tracking is equally difficult because of the unreliable appearance, and the unreliable motion estimation. Luckily, we found that by combining this two challenging tasks together, there will be mutual benefits. Following the idea, in this paper, we present a neural network model called the Recurrent Correlational Netw…
DPNet: Dynamic Pooling Network for Tiny Object Detection
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机科学学院）；Research Center for Space Computing System, Zhejiang Lab（浙江实验室空间计算系统研究中心）；SWJTU-LEEDS Joint School, Southwest Jiaotong University（西南交通大学SWJTU-LEEDS联合学院）；Guangdong Zhiyun City construction Technology Co., LTD（广东智云城市建设技术有限公司）；Department of Control Science and Engineering, College of Electronic and Information Engineering, Tongji University（同济大学控制科学与工程系）；Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University（河海大学航海智能网络信息科技重点实验室）
Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of capturing aerial (bird-view) images. The availability of aerial visual data and the recent advances in object detection algorithms led the computer vision community to focus on object detection tasks on aerial images. As a result of this, several aerial datasets have been introduced, including visual data with object annotations. UAVs are used solely as flying-cameras in these datasets, discarding different data types regarding the flight (e.g., time, location, internal sensors). In this work, we propose a multi-purpose…
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny objects due to the lack of supervision from discriminative features. Our key observation is that the Intersection over Union (IoU) metric and its extensions are very sensitive to the location deviation of the tiny objects, which drastically deteriorates the quality of label assignment when used in anchor-based detectors. To tackle this problem, we propose a new evaluation metric dubbed Normalized Wasserstein Di…
Inception Institute of Artificial Intelligence, UAE（阿联酋Inception人工智能研究所）；Wuhan University, China（中国武汉大学）；Huazhong University of Science and Technology, China（中国华中科技大学）
Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object ins…
Agricultural Information Institute, Chinese Academy of Agricultural Sciences（中国农业科学院农业信息研究所）；Leibniz Institute for Agricultural Engineering and Bioeconomy（莱布尼茨农业工程与生物经济研究所）
In precision crop protection, (target-orientated) object detection in image processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection drones) to the right place to apply the pesticide. Unnecessary application of non-target areas could be avoided. Deep learning algorithms dominantly use in modern computer vision tasks which require high computing time, memory footprint, and power consumption. Based on the Edge Artificial Intelligence, we investigate the main three paths that lead to dealing with this problem, including hardware accelerators, efficient algorithms, and model com…
Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional n…
Memory-Augmented Multimodal Large Language Models for Small Object Understanding in Streaming Aerial Videos
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Freiburg（弗莱堡大学）；Hangzhou City University（杭州城市大学）；XGRIDS（XGRIDS公司）；Fudan University（复旦大学）；Hong Kong Baptist University（香港浸会大学）
Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal)…
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…
Computer Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM)（计算机工程系，国王法赫德石油与矿物大学）；SDAIA-KFUPM Joint Research Center on Artificial Intelligence, IRC for Intelligent Secure Systems and Department of Computer Engineering, KFUPM（SDAIA-KFUPM人工智能联合研究中心，智能安全系统研究院及计算机工程系，KFUPM）；Center for Intelligent Secure Systems and the Department of Computer Engineering, King Fahd University of Petroleum & Minerals（智能安全系统中心及计算机工程系，国王法赫德石油与矿物大学）；Research Chair of Pervasive and Mobile Computing, Department of Software Engineering, College of Computer and Information Sciences, King Saud University（普及与移动计算研究主席，软件工程系，计算机与信息科学学院，国王沙特大学）
本文提出TinyEcoWeedNet，通过结构化剪枝、量化感知训练和TensorRT加速，在Jetson Orin Nano上实现184 FPS实时杂草检测，模型缩小68.5%，并在CottonWeedDet12上以85.9% mAP50超越YOLO11n和YOLO12n。
DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）
AIT Austrian Institute of Technology, Center for Digital Safety & Security（奥地利技术学院（AIT）数字安全与保障中心）；TU Wien, Institute of Visual Computing & Human-Centered Technology（维也纳技术大学视觉计算与以人类为中心技术研究所）
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moder…
Visuals captured by high-flying aerial drones are increasingly used to assess biodiversity and animal population dynamics around the globe. Yet, challenging acquisition scenarios and tiny animal depictions in airborne imagery, despite ultra-high resolution cameras, have so far been limiting factors for applying computer vision detectors successfully with high confidence. In this paper, we address the problem for the first time by combining deep object detectors with super-resolution techniques and altitude data. In particular, we show that the integration of a holistic attention network based …
