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AG-Yolo: Attention-Guided Yolo for Efficient Remote Sensing oriented object detection

REMOTE SENSING 2025年第6期17卷 1027-1027页

作者： Wang, Xiaofeng Han, Chengshan Huang, Liang Nie, Ting Liu, Xin Liu, Hao Li, Mingxuan Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Remote sensing can efficiently acquire information and is widely used in many areas. object detection is a key component in most applications. But complex backgrounds in remote sensing images severely degrade detection performance. Current methods fail to effectively suppress background interference while maintaining fast detection speeds. This paper proposes Attention-Guided Yolo (AG-Yolo), an efficient oriented object detection (OOD) method tailored for remote sensing. AG-Yolo incorporates an additional rotation parameter into the head of Yolo-v10 and extends its dual label assignment strategy to maintain high efficiency in OOD. An attention branch is further introduced to generate attention maps from shallow input features, guiding feature aggregation to focus on foreground objects and suppress complex background interference. Additionally, derived from the background complexity, a three-stage curriculum learning strategy is designed to train the model from some much easier samples generated from the labeled data. This approach can give the model a better starting point, improving its ability to handle complicated datasets and increasing detection precision. On the DOTA-v1.0 and DOTA-v1.5 datasets, compared with other advanced methods, our algorithm reduces the processing latency from 33.8 ms to 19.7 ms (a roughly 40% decrease) and produces a certain degree of improvement in the mAP metric.

关键词： remote sensing oriented object detection attention curriculum learning

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Attention-Based Mean-Max Balance Assignment for oriented object detection in Optical Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Lin, Qifeng Chen, Nuo Huang, Haibin Zhu, Daoye Fu, Gang Chen, Chuanxi Yu, Yuanlong Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Univ Toronto Dept Geog Geomatics & Environm Mississauga ON L5L 1C6 Canada Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Fujian Peoples R China

For objects with arbitrary angles in optical remote sensing (RS) images, the oriented bounding box regression task often faces the problem of ambiguous boundaries between positive and negative samples. The statistical analysis of existing label assignment strategies reveals that anchors with low Intersection over Union (IoU) between ground truth (GT) may also accurately surround the GT after decoding. Therefore, this article proposes an attention-based mean-max balance assignment (AMMBA) strategy, which consists of two parts: mean-max balance assignment (MMBA) strategy and balance feature pyramid with attention (BFPA). MMBA employs the mean-max assignment (MMA) and balance assignment (BA) to dynamically calculate a positive threshold and adaptively match better positive samples for each GT for training. Meanwhile, to meet the need of MMBA for more accurate feature maps, we construct a BFPA module that integrates spatial and scale attention mechanisms to promote global information propagation. Combined with S2ANet, our AMMBA method can effectively achieve state-of-the-art performance, with a precision of 80.91% on the DOTA dataset in a simple plug-and-play fashion. Extensive experiments on three challenging optical RS image datasets (DOTA-v1.0, HRSC, and DIOR-R) further demonstrate the balance between precision and speed in single-stage object detectors. Our AMMBA has enough potential to assist all existing RS models in a simple way to achieve better detection performance. The code is available at https://***/promisekoloer/AMMBA.

关键词： Remote sensing Detectors Feature extraction object detection Training Semantics Location awareness Accuracy Shape Optical scattering Attention feature fusion label assignment optical remote sensing (RS) images oriented object detection

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Center-Symmetry Representation-Based High-Quality Localization Detector for oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Wu, Qin Zhang, Dabin Pan, Yuying Zhou, Haojie Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214200 Peoples R China

Two-stage detectors are widely used in oriented object detection and have achieved high detection accuracy. However, some inconsistency issues in two-stage detectors limit their further improvement. To overcome these issues, this work proposes an effective two-stage center-symmetry representation-based high-quality localization detector (CR-HLDet) for oriented objects in remote sensing images. It improves the three main components of a typical two-stage detector: neck, region proposal network (RPN), and head. Specifically, in the neck, we introduce a feature enhancement block (FEB) to extract multilevel semantic information to solve the inconsistency in feature fusion. And we use a novel center-symmetry representation to generate high-quality oriented proposals in the RPN. Instead of the prediction of the rotated angle, it utilizes the symmetry of the center point to locate any oriented bounding box (OBB) by three points, which gets rid of the inconsistency between proposals and oriented objects. Finally, we design a dual classification detection head (DCDH) to obtain more robust and reasonable classification confidence. It solves the inconsistency between classification confidence and the regression quality by establishing the connection between the classification branch and the regression branch. The effective combination of the above three modules enables our CR-HLDet to accurately locate oriented objects in remote sensing images. Extensive and comprehensive experiments on three challenging remote sensing datasets (DOTA, HRSC2016, and DIOR-R) demonstrate that the proposed method achieves state-of-the-art performance.

关键词： Feature extraction Proposals Detectors object detection Head Location awareness Semantics Accuracy Remote sensing Neck Center-symmetry representation dual classification detection head (DCDH) high-quality localization multilevel semantic information oriented object detection

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Boosting Point Set-Based Network with Optimal Transport Optimization for oriented object detection

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REMOTE SENSING 2024年第22期16卷 4133-4133页

作者： Yuan, Binhuan Zhi, Xiyang Hu, Jianming Zhang, Wei Harbin Inst Technol Res Ctr Space Opt Engn Harbin 150001 Peoples R China

When handling complex remote sensing scenarios, rotational angle information can improve detection accuracy and enhance algorithm robustness, providing support for fine-grained detection. Point set representation is one of the most commonly used methods in arbitrary-oriented object detection tasks, leveraging discrete feature points to represent oriented targets and achieve high accuracy in angle prediction. However, due to the inherent discreteness of point set representation, it is prone to significant impact from isolated points and representational ambiguity in harsh application scenarios, leading to inaccurate detection. To address this issue, an efficient aerial object detector named BE-Det is proposed, which uses the optimal transport (OT) strategy to constrain the positions of isolated points. Additionally, a candidate point set quality evaluation scheme is designed to effectively assess the quality of candidate point sets. Experimental results on two challenging aerial datasets demonstrate that the proposed method outperforms several advanced detection methods.

关键词： point set oriented object detection optimal transport quality evaluation

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A2Net: An Anchor-Free Alignment Network for oriented object detection in Remote Sensing Images

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IEEE ACCESS 2024年 12卷 42017-42027页

作者： Yang, Qingyong Cao, Likun Huang, Chenchen Song, Qi Yuan, Chunmiao Tiangong Univ Sch Software Tianjin 300387 Peoples R China Tianjin Sino German Univ Appl Sci Sch Software & Commun Tianjin Peoples R China

object detection in remote sensing images is crucial for identifying and locating objects in the field, holding significance in remote sensing. oriented object detection employs oriented bounding boxes to locate objects with varying orientations, achieving recent advancements. However, challenges persist due to vast variations in object scale and orientation. Existing methods use Intersection over Union (IoU) to measure bounding box quality but often ignore shape information. Unlike horizontal detectors, oriented detectors always incorporate an angle parameter. Yet, objects with different shapes exhibit varying angle sensitivity. For objects with the same angle but different shapes, their IoU can differ significantly. We argue that relying solely on IoU is not comprehensive. To address this, we propose the Shape-aware IoU Score (SaIS), considering shape information and IoU for each bounding box. We use SaIS to enhance the dynamic soft label assignment strategy, resulting in an improved Shape-aware Label Assignment (SaLA). SaLA aids the detector in selecting more suitable samples. Leveraging RTMDet-R and S2ANet strengths, we design an Anchor-free Alignment Network (A2Net) for oriented object detection. A2Net features two detection heads: the initial head and the refinement head. Utilizing alignment convolution (AlignConv) between these heads obtains aligned features. We validate the proposed approach's effectiveness on the DOTA dataset and DIOR-R dataset.

关键词： oriented object detection shape-aware remote sensing deep learning

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S²O-Det: A Semisupervised oriented object detection Network for Remote Sensing Images

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 11285-11294页

作者： Fu, Ronghao Yan, Shuang Chen, Chengcheng Wang, Xianchang Heidari, Ali Asgher Li, Jing Chen, Huiling Jilin Univ Coll Comp Sci & Technol Changchun 130021 Peoples R China Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130021 Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun 130021 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework ((SO)-O-2-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that (SO)-O-2-Det achieves promising performance across different labeling ratios.

关键词： oriented object detection remote sensing image semisupervised learning

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Global Focal Learning for Semi-Supervised oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Kai Xiao, Zhifeng Wan, Qiao Xia, Fanfan Chen, Pin Li, Deren Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

oriented object detectors have achieved great success in aerial detection tasks with the help of ample labeled data. Unlabeled images are easier and less expensive to obtain than labeled aerial images. Therefore, semi-supervised oriented object detection (SSOOD) is becoming a hot task, which can leverage both labeled and unlabeled data to train oriented detectors. Most SSOOD approaches focus on well-designed approaches to generate high-quality pseudo labels (PLs) or positive learning regions, which are limited to complex and variable aerial scenes. This study first analyzes key factors influencing the performance of SSOOD and proposes a global focal learning method (termed as focal teacher) without artificial priori design. It relies on global region and soft regression approaches to blur the boundaries between positive and negative samples, mainly through localization focal loss to achieve. It leverages the localization consistency between the teacher and student model to focus more on hard regions. Moreover, we organize a large remote sensing unlabeled (RSUL) dataset to exploit the performance potential of oriented detectors on mainstream aerial detection datasets (DOTA and DIOR). Adequate experiments reveal that the proposed method achieves the best performance compared with other mainstream SSOOD methods, including partly, fully, and additional data settings on DOTA and DIOR datasets. Semi-supervised mechanisms without preset learning regions can be better applied in dense and complex aerial scenes.

关键词： Detectors object detection Remote sensing Task analysis Location awareness Data models Semisupervised learning Aerial image anchor-free detector oriented object detection semi-supervised learning (SSL)

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On Boundary Discontinuity in Angle Regression Based Arbitrary oriented object detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第10期46卷 6494-6508页

作者： Yu, Yi Da, Feipeng Southeast Univ Sch Automat Nanjing 211189 Peoples R China

With vigorous development e.g., in autonomous driving and remote sensing, oriented object detection has gradually been featured. The majority of existing methods directly perform regression on the rotation angle, which we argue has fundamental limitations of boundary discontinuity (even if using Gaussian or RotatedIoU-based losses). In this paper, a novel angle coder named phase-shifting coder (PSC) is proposed to address this issue. Different from another well-explored alternative i.e., angle classification, PSC achieves boundary-discontinuity-free in a continuous and differentiable manner and thus can work together with Gaussian or RotatedIoU-based methods to further boost their performance. Moreover, by rethinking the boundary discontinuity of elongated and square-like objects as rotational symmetry of different cycles, a dual-frequency version (PSCD) is proposed to accurately predict the orientation of both types of objects. Visual analysis and extensive experiments on several popular backbone detectors and datasets demonstrate the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance.

关键词： Feature extraction Detectors Proposals Artificial neural networks YOLO Visualization Codes Angle regression boundary discontinuity oriented object detection phase-shifting coder

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Channel Self-Attention Based Multiscale Spatial-Frequency Domain Network for oriented object detection in Remote Sensing Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Xu, Yang Pan, Yushan Wu, Zebin Wei, Zhihui Zhan, Tianming Nanjing Univ Sci & Technol NJUST Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Audit Univ Jiangsu Key Construct Lab Audit Informat Engn Nanjing 211815 Peoples R China Nanjing Audit Univ Sch Informat Engn Nanjing 211815 Peoples R China

The detection of oriented objects in remote sensing images remains a daunting challenge due to their complex backgrounds, various sizes, and especially arbitrary orientations. However, most of the existing methods only model the structural features of the images in the spatial domain, while the horizontal convolution kernels limit the model's ability to perceive object direction information. Furthermore, the frequency features contain rich information about scale, texture, and angle, which can be a good complement to the spatial features. Inspired by this, we propose a multiscale spatial-frequency domain network (MSFN) to utilize spatial-frequency information for oriented object detection, which can be integrated into any convolutional neural network (CNN) architectures seamlessly and perform end-to-end training easily. Firstly, multiscale Haar wavelet transforms are leveraged to extract the multiscale frequency domain features from the image. Subsequently, channel alignment feature fusion module (CA-FFM) is proposed to fuse the high-level semantic features extracted by CNN with the low-level texture features extracted by the wavelet transform in multiscale. Finally, a channel self-attention (CSA)-based spatial-frequency feature perception module (SFPM) is designed to perform self-attention weighted aggregation on the fused features along the channel dimension, thereby constructing a novel spatial-frequency feature extraction backbone network for oriented object detector in remote sensing images. Experimental results on the DOTA and HRSC2016 datasets validate the effectiveness and universality of the proposed method.

关键词： Feature extraction Frequency-domain analysis Detectors Remote sensing object detection Data mining Attention mechanisms Wavelet transforms Convolution Semantics Fusion features Haar wavelet transform oriented object detection remote sensing imagery spatial-frequency domain

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Feature Enhancement Based oriented object detection in Remote Sensing Images

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NEURAL PROCESSING LETTERS 2024年第6期56卷 244页

作者： Guo, Hongjian Zhou, Xianlin Yang, Peng Nanjing Audit Univ Sch Comp Sci Nanjing 211815 Peoples R China

Since objects in remote sensing imagery often have arbitrary orientations and high densities, the features of small objects are inclined to be contaminated by the background and other instances. To address the issues, we propose a new oriented object detection framework where a series of feature enhancement schemes are implemented so as to improve robustness and accuracy of the detector. Firstly, we design a weighted bidirectional feature pyramid network, which can be used to fuse both high-level semantic features and low-level detail features for effectively handling with multi-scale objects. Accordingly, we apply the convolutional block attention module that exploits both spatial- and channel-wise attention in our detector, and study how to effectively integrate it into the framework for adaptive feature refinement. In the meanwhile, we present a semantic segmentation guided module to generate naive mask, which is used to multiple with pyramid features to filter out background noise and improve feature representation for small objects. The experimental results on two public datasets, i.e., UCAS-AOD and DOTA, validate the effective performance of the proposed method for oriented object detection in remote sensing images.

关键词： oriented object detection Deep learning Feature enhancement Remote sensing images

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