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oriented object detection in satellite images using convolutional neural network based on ResNeXt

ETRI JOURNAL 2024年第2期46卷 307-322页

作者： Haryono, Asep Jati, Grafika Jatmiko, Wisnu Univ Indonesia Fac Comp Sci Depok Indonesia Natl Res & Innovat Agcy Res Ctr Artificial Intelligence & Cyber Secur Jakarta Indonesia

Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multibranch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.

关键词： box-boundary-aware vector convolutional neural network oriented object detection ResNeXt101 satellite imagery

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oriented object detection Based on Adaptive Feature Learning and Enrichment

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2735-2739页

作者： Li, Pei Zhu, Zhongjie Bai, Yongqiang Wang, Yuer Zhang, Lei Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo 315000 Peoples R China

oriented object detection has broad utilization in many fields, including urban traffic monitoring, land utilization assessment, and environmental monitoring. However, current oriented object detecting methods are limited in leveraging multiscale information, failing to fully exploit the rich scale variation within images and resulting in suboptimal performance when detecting multiscale targets. Herein, an innovative method SH-Net is proposed based on adaptive feature learning and enrichment. First, an adaptive feature learning module (AFLM) is constructed to enhance the feature learning capability for multiscale objects. Second, a high-resolution feature pyramidal network (HRFPN) is constructed to enhance deep feature fusion for dense and small targets. Finally, a rotated proposal generation (RPG) module and rotated box refinement (RBR) module are proposed to generate and refine the bounding box for extracted oriented objects. The experimental results obtained on the DOTA dataset show that SH-Net can achieve a mAP of 82.67% and surpasses most state-of-the-art methods.

关键词： Feature extraction Convolution object detection Representation learning Proposals Accuracy Semantics Remote sensing Deep learning Switches Convolutional neural networks feature pyramid oriented object detection

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oriented object detection in Remote Sensing Images with Anchor-Free oriented Region Proposal Network

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REMOTE SENSING 2022年第5期14卷 1246-1246页

作者： Li, Jianxiang Tian, Yan Xu, Yiping Zhang, Zili Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

oriented object detection is a fundamental and challenging task in remote sensing image analysis that has recently drawn much attention. Currently, mainstream oriented object detectors are based on densely placed predefined anchors. However, the high number of anchors aggravates the positive and negative sample imbalance problem, which may lead to duplicate detections or missed detections. To address the problem, this paper proposes a novel anchor-free two-stage oriented object detector. We propose the Anchor-Free oriented Region Proposal Network (AFO-RPN) to generate high-quality oriented proposals without enormous predefined anchors. To deal with rotation problems, we also propose a new representation of an oriented box based on a polar coordinate system. To solve the severe appearance ambiguity problems faced by anchor-free methods, we use a Criss-Cross Attention Feature Pyramid Network (CCA-FPN) to exploit the contextual information of each pixel and its neighbors in order to enhance the feature representation. Extensive experiments on three public remote sensing benchmarks-DOTA, DIOR-R, and HRSC2016-demonstrate that our method can achieve very promising detection performance, with a mean average precision (mAP) of 80.68%, 67.15%, and 90.45%, respectively, on the benchmarks.

关键词： remote sensing images oriented object detection contextual information Anchor Free Region Proposal Network polar representation

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for oriented object detection

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Computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for oriented object detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： oriented object detection transformer deep learning

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RQFormer: Rotated Query Transformer for end-to-end oriented object detection

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Zhao, Jiaqi Ding, Zeyu Zhou, Yong Zhu, Hancheng Du, Wen-Liang Yao, Rui El Saddik, Abdulmotaleb China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Innovat Res Ctr Disaster Intelligent Prevent & Eme Xuzhou 221116 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

oriented object detection presents a challenging task due to the presence of object instances with multiple orientations, varying scales, and dense distributions. Recently, end-to-end detectors have made significant strides by employing attention mechanisms and refining a fixed number of queries through consecutive decoder layers. However, existing end-to-end oriented object detectors still face two primary challenges: (1) misalignment between positional queries and keys, leading to inconsistency between classification and localization;and (2) the presence of a large number of similar queries, which complicates one-to-one label assignments and optimization. To address these limitations, we propose an end-to-end oriented detector called the Rotated Query Transformer, which integrates two key technologies: Rotated RoI Attention (RRoI Attention) and Selective Distinct Queries (SDQ). First, RRoI Attention aligns positional queries and keys from oriented regions of interest through cross-attention. Second, SDQ collects queries from intermediate decoder layers and filters out similar ones to generate distinct queries, thereby facilitating the optimization of one-to-one label assignments. Finally, extensive experiments conducted on four remote sensing datasets and one scene text dataset demonstrate the effectiveness of our method. To further validate its generalization capability, we also extend our approach to horizontal object detection. The code is available at https://***/ wokaikaixinxin/RQFormer.

关键词： oriented object detection Transformer End-to-end detectors Attention Query update

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GOOD: Towards domain generalized oriented object detection

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 223卷 207-220页

作者： Bi, Qi Zhou, Beichen Yi, Jingjun Ji, Wei Zhan, Haolan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Univ Amsterdam Fac Sci NL-1098XH Amsterdam Netherlands Yale Univ Sch Med New Haven CT 06520 USA Monash Univ Fac Informat Technol Melbourne Vic 3800 Australia

oriented object detection has been rapidly developed in the past few years, but most of these methods assume the training and testing images are under the same statistical distribution, which is far from reality. In this paper, we propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object detectors on arbitrary unseen target domains. Learning domain generalized oriented object detectors is particularly challenging, as the cross-domain style variation not only negatively impacts the content representation, but also leads to unreliable orientation predictions. To address these challenges, we propose a generalized oriented object detector (GOOD). After style hallucination by the emerging contrastive language-image pre-training (CLIP), it consists of two key components, namely, rotation- aware content consistency learning (RAC) and style consistency learning (SEC). The proposed RAC allows the oriented object detector to learn stable orientation representation from style-diversified samples. The proposed SEC further stabilizes the generalization ability of content representation from different image styles. Notably, both learning objectives are simple, straight-forward and easy-to-implement. Extensive experiments on multiple cross-domain settings show the state-of-the-art performance of GOOD. Source code is available at https://***/BiQiWHU/GOOD.

关键词： oriented object detection Domain generalization Rotation consistency Style hallucination

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EOOD: End-to-end oriented object detection

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NEUROCOMPUTING 2025年 621卷

作者： Zhang, Caiguang Chen, Zilong Xiong, Boli Ji, Kefeng Kuang, Gangyao Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effec Changsha 410073 Peoples R China Shanghai Radio Equipment Res Inst Shanghai 201100 Peoples R China

Recently, significant advancements have been made in oriented object detectors based on convolutional networks. However, these models often rely on hand-designed post-processing techniques such as non- maximum suppression (NMS) to suppress redundant predictions, which impedes establishing an end-to-end detection system. In this paper, we explore how to build an end-to-end oriented detector? Firstly, our research demonstrates that prediction-oriented one-to-one label assignment (POLA) can significantly reduce the performance gap between using and not using NMS and is an essential component of end-to-end detection. Additionally, the proposed Negative Sample Reweighted Focal Loss ( N'FL ) can widen the classification confidence gap between positive and negative samples, separate positive samples from noise, and guarantee high classification scores for the single positive sample. Finally, in order to address the lack of supervision caused by one-to-one label assignment, a joint training pipeline is designed that unites multiple auxiliary heads and takes advantage of one-to-many label assignment on supervision to improve feature representation and increase performance. During inference, Only the main head trained with one-to-one label assignment is involved in prediction. Extensive experiments on publicly available datasets DOTA and DIOR-R demonstrate that the proposed EOOD exhibits significant performance improvements over baseline models and has the potential to overcome the limitations of NMS. Code is available at https://***/zhangiguang/EOOD.

关键词： oriented object detection Label assignment End-to-end Non-maximum suppression

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GCA2Net: Global-Consolidation and Angle-Adaptive Network for oriented object detection in Aerial Imagery

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REMOTE SENSING 2025年第6期17卷 1077-1077页

作者： Zhou, Shenbo Liu, Zhenfei Luo, Hui Qi, Guanglin Liu, Yunfeng Zuo, Haorui Zhang, Jianlin Wei, Yuxing Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Enhancing the detection capabilities of rotated objects in aerial imagery is a vital aspect of the burgeoning field of remote sensing technology. The objective is to identify and localize objects oriented in arbitrary directions within the image. In recent years, the capacity for rotated object detection has seen continuous improvement. However, existing methods largely employ traditional backbone networks, where static convolutions excel at extracting features from objects oriented at a specific angle. In contrast, most objects in aerial imagery are oriented in various directions. This poses a challenge for backbone networks to extract high-quality features from objects of different orientations. In response to the challenge above, we propose the Dynamic Rotational Convolution (DRC) module. By integrating it into the ResNet backbone network, we form the backbone network presented in this paper, DRC-ResNet. Within the proposed DRC module, rotation parameters are predicted by the Adaptive Routing Unit (ARU), employing a data-driven approach to adaptively rotate convolutional kernels to extract features from objects oriented in various directions within different images. Building upon this foundation, we introduce a conditional computation mechanism that enables convolutional kernels to more flexibly and efficiently adapt to the dramatic angular changes of objects within images. To better integrate key information within images after obtaining features rich in angular details, we propose the Multi-Order Spatial-Channel Aggregation Block (MOSCAB) module, which is aimed at enhancing the integration capacity of key information in images through selective focusing and global information aggregation. Meanwhile, considering the significant semantic gap between features at different levels during the feature pyramid fusion process, we propose a new multi-scale fusion network named AugFPN+. This network reduces the semantic gap between different levels before feature fusion,

关键词： oriented object detection feature extraction feature enhancement convolutional neural network remote sensing image

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Domain Adaptive oriented object detection From Optical to SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Huang, Hailiang Guo, Jingchao Lin, Huangxing Huang, Yue Ding, Xinghao Xiamen Univ Sch Informat Xiamen 361021 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

oriented object detection in synthetic aperture radar (SAR) images presents significant challenges due to the scarcity of labeled data. In contrast, acquiring labeled optical remote sensing images is considerably easier. This article proposes a domain adaptive oriented object detection (DAOOD) model, termed the pixel-instance information transfer-based model (PITM). PITM aims to transfer knowledge from optical to SAR domains, thereby reducing the dependency of oriented SAR object detection on labels. Given the pronounced domain disparity between optical and SAR images, the efficient migration of both visual content and rotating instances is incorporated to bridge the gap in their information distribution simultaneously. Specifically, regarding pixel-level information transfer, speckle noise from SAR images is mixed into the optical domain to form an intermediate domain, thus compensating for the visual difference between the two domains. For instance-level information transfer, considering the angle diversity of rotating objects, multiscale and multidirectional spatial information extraction is combined with decoupled instance-invariant features, enhancing the cross-domain discernment capacity of rotating instances. Experimental results on four DAOOD benchmarks (i.e., two optical datasets to two SAR datasets) demonstrate that the proposed PITM significantly improves oriented object detection performance, even in the absence of labeled SAR images. Specifically, it individually outperforms two source-only models by 88.16% and 54.62% in average precision (AP).

关键词： object detection Optical sensors Optical imaging Remote sensing Adaptive optics Integrated optics Synthetic aperture radar Radar polarimetry Adaptation models Feature extraction Decoupled instance Gabor convolutional networks (GCNs) oriented object detection synthetic aperture radar (SAR) unsupervised domain adaption

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Dual Teacher: Improving the Reliability of Pseudo Labels for Semi-Supervised oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Fang, Zhenyu Ren, Jinchang Zheng, Jiangbin Chen, Rongjun Zhao, Huimin Northwestern Polytech Univ Sch Software Xian 710060 Peoples R China NPU Yangtze River Delta Res Inst Suzhou 215400 Taicang Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China Robert Gordon Univ Natl Subsea Ctr Aberdeen AB21 0BH Scotland

oriented object detection in remote sensing is a critical task for accurately location and measurement of the interested targets. Despite of its success in object detection, deep learning-based detectors rely heavily on extensive data annotation. However, variations in object appearance significantly increase the difficulty and the cost of creating large-scale annotated datasets. Semi-supervised learning (SSL) aims to utilize unlabeled data to enhance object detectors. Among these, pseudo-label-based methods have shown promising results recently. Nonetheless, as training progresses, the accumulation of errors in pseudo labels leads to prediction bias without corrections. To tackle this particular challenge, we present a SSL pipeline, named "dual teacher," for improving the reliability of pseudo labels in the semi-supervised oriented object detection. First, to mitigate the bias caused by limited annotated data, a global burn-in (GBI) strategy is introduced at the beginning of training, which guides the student detector to learn the feature extraction on a global scale. In addition, an online bounding box (bbox) correction module is proposed to decrease the occurrence of mislabeled instances and enhance the reliability of detection. These improvements are facilitated by an additional detector, instead of a single teacher model in the teacher-student architecture. Dual teacher reduces the dependency on the quality of pseudo labels related to the model complexity and combines the strengths of both the two-stage and one-stage detectors. With only 20% labeled data, dual teacher outperforms fully supervised rotated fully convolutional one-stage object detection (R-FCOS), you only look once X-small (YOLOX-s), and rotated region-based convolutional neural network (R-RCNN) by up to 2% on both a large-scale dataset for object detection in aerial images (DOTA) and SODA-A datasets. This reveals its potential in reducing labor-intensive tasks and enhancing robustness against env

关键词： Detectors Remote sensing object detection Training Feature extraction Annotations Semisupervised learning Predictive models Supervised learning Interference Consistency learning dual teacher oriented object detection pseudo label semi-supervised learning (SSL)

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