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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Qingyang Cheng, Gong Miao, Shicheng Pei, Lei Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Current two-stage oriented object detectors for aerial images have achieved remarkable progress. However, they still suffer from some drawbacks. Firstly, most of them place redundant anchors or utilize complicated transformation to generate oriented proposals, which are inefficient. Secondly, the generation of proposals is static, which cannot adapt to the extremely nonuniform distribution of objects. To address these issues, we propose a Dynamic Proposal Generation Network (DPGN) which can generate high-quality oriented proposals directly and estimate the upper limit of proposals adaptively. To be specific, with Guided Anchor Regression (GAR), we obtain the coarse oriented anchors and utilize them to align the features. After this, we make further classification and regression to produce final oriented proposals. Meanwhile, we design Maximum Number Estimation (MNE) for predicting an approximate value to remain the proposals adaptively. Without tricks, our method can achieve competitive detection accuracy compared with other mainstream methods on DOTA dataset.

关键词： oriented object detection Dynamic Proposal Generation Aerial Images

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Learning Critical Features for Arbitrary-oriented object detection in Remote-Sensing Optical Images

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Sun, Peng Zheng, Yongbin Wu, Wenqi Xu, Wanying Bai, Shengjian Lu, Xiaoping Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Arbitrary-oriented object detection in remote-sensing optical images is an emerging, yet challenging task due to the diversity of the visual appearance of objects. Although considerable progress has been made, existing methods often suffer from insufficient positive samples and critical feature misalignment caused by objects with considerable diversity. In this article, we propose a critical feature learning (CFL) method to address the above issues. First, to obtain a more accurate location, we introduce the spatial transform to model the rotating bounding box regression and construct a spatial transform selection (STS) strategy, which is used to implement dynamic sample selection metrics to guarantee sufficient positive samples. Then, we propose a scale-controlled smooth L-1 loss (SC-Loss) to change the form of regression loss function based on the statistics of proposals so that the training can focus more on high-quality samples. Finally, to capture critical features, we propose a critical feature sampling (CFS) module to perform location refinement of classification feature samples to extract accurate features. Comprehensive and extensive experimental results based on three benchmarks, DOTA, FAIR1M-1.0, and HRSC2016, demonstrate that the proposed method is superior to existing methods in terms of accuracy and effectiveness.

关键词： Classification feature sampling critical feature learning (CFL) dynamic label assignment objects with huge diversity oriented object detection

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DRDet: Dual-Angle Rotated Line Representation for oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Zhang, Minjian Qiu, Heqian Mei, Hefei Wang, Lanxiao Meng, Fanman Xu, Linfeng Li, Hongliang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

In aerial scenes, oriented object detection is sensitive to the orientation of objects, which makes the formulation of orientation-aware object representation become a critical problem. Existing methods mostly adopt rectangle anchors or discrete points as object representation, which may lead to the feature aliasing between overlapping objects and ignore the orientation information of objects. To solve these issues, we propose a novel anchor-free oriented object detection network named DRDet, which adopts dual-angle rotated lines (DRLs) as object representation. Different from other object representations, DRL can adaptively rotate and extend to the boundary of the object according to its orientation and shape, which explicitly introduces the orientation information into the formulation of object representation. And it can adaptively cope with the geometric deformation of objects. Based on the DRLs, we design an orientation-guided feature encoder (OFE) to encode discriminant object features along each rotated line, respectively. Instead of encoding the rectangle feature, the OFE module adopts line features for orientation-guided feature encoding, which can alleviate the feature aliasing between neighboring objects or backgrounds. To further enhance the flexibility of DRLs, we design a dual-angle decoder (DD) that predicts two angle offsets according to the orientation-guided feature and converts the angle offsets and regression offsets into DRL representation, which can help to guide the adaptive rotation of each rotated line, respectively. Our proposed method achieves consistent improvement on both DOTA and HRSC2016 datasets. Extensive experiment results verify the effectiveness of our method in oriented object detection.

关键词： Index Terms- Aerial scenes feature extraction object repre-sentation oriented object detection

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Dynamic Cascade Query Selection for oriented object detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1-1页

作者： Zeng, Qiaolin Ran, Xiang Zhu, Hao Gao, Yanghua Qiu, Xinfa Chen, Liangfu Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Inst Meteorol Sci Chongqing 401147 Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Sci Nanjing 210044 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China

Most of the existing object detection methods have complicated hand-designed components, such as nonmaximum suppression procedures and manual resizing of anchor boxes. Based on detection transformer (DETR), this letter not only eliminates the need for manual component adjustment but also solves three problems of poor remote sensing image for directional object capture, slow DETR convergence, and the same attention allocated by different layers of decoder. First, the D-angle module is used to align the rotating object region while accelerating the convergence using the a priori angle. Then, the overall computation of the model is reduced by using adaptive proposal selection (APS) in the cascade structure. Finally, the adaptive query selection (AQS) module is applied so that the decoder in different layers gets different attention weights to optimize the layer-by-layer fine-tuning process. In this letter, the effectiveness of the proposed method is verified using two public datasets, DOTA and HRSC2016.

关键词： Attention mechanism decoder oriented object detection

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On Improving Bounding Box Representations for oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Yao, Yanqing Cheng, Gong Wang, Guangxing Li, Shengyang Zhou, Peicheng Xie, Xingxing Han, Junwei Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Aeronaut & Astronaut Beijing 100049 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Detecting objects in remote sensing images (RSIs) using oriented bounding boxes (OBBs) is flourishing but challenging, wherein the design of OBB representations is the key to achieving accurate detection. In this article, we focus on two issues that hinder the performance of the two-stage oriented detectors: 1) the notorious boundary discontinuity problem, which would result in significant loss increases in boundary conditions, and 2) the inconsistency in regression schemes between the two stages. We propose a simple and effective bounding box representation by drawing inspiration from the polar coordinate system and integrate it into two detection stages to circumvent the two issues. The first stage specifically initializes four quadrant points as the starting points of the regression for producing high-quality oriented candidates without any postprocessing. In the second stage, the final localization results are refined using the proposed novel bounding box representation, which can fully release the capabilities of the oriented detectors. Such consistency brings a good trade-off between accuracy and speed. With only flipping augmentation and single-scale training and testing, our approach with ResNet-50-FPN harvests 76.25% mAP on the DOTA dataset with a speed of up to 16.5 frames/s, achieving the best accuracy and the fastest speed among the mainstream two-stage oriented detectors. Additional results on the DIOR-R and HRSC2016 datasets also demonstrate the effectiveness and robustness of our method. The source code is publicly available at https://***/yanqingyao1994/QPDet.

关键词： Proposals object detection Detectors Feature extraction Remote sensing Location awareness Training oriented object detection quadrant point regression rotated box refinement rotated proposal generation

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MKIoU loss: toward accurate oriented object detection in aerial images

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JOURNAL OF ELECTRONIC IMAGING 2023年第3期32卷 033030-033030页

作者： Yu, Xinyi Lu, Jiangping Lin, Mi Zhou, Libo Ou, Linlin Zhejiang Univ Technol Hangzhou Peoples R China

oriented bounding box regression is crucial for oriented object detection. However, regression-based methods often suffer from boundary problems and the inconsistency between loss and evaluation metrics. A modulated Kalman intersection over union (IoU) loss of approximate SkewIoU is proposed, named MKIoU. To avoid boundary problems, we convert the oriented bounding box to Gaussian distribution then use the Kalman filter to approximate the intersection area. However, there exists significant difference between the calculated and actual intersection areas. Thus, we propose a modulation factor to adjust the sensitivity of angle deviation and width-height offset to loss variation, making the loss more consistent with the evaluation metric. Furthermore, the Gaussian modeling method avoids the boundary problem but causes the angle confusion of square objects simultaneously. Thus, the Gaussian angle loss (GA loss) is presented to solve this problem by adding a corrected loss for square targets. The proposed GA loss can be easily extended to other Gaussian-based methods. Experiments on three publicly available aerial image datasets, DOTA, UCAS-AOD, and HRSC2016, show the effectiveness of the proposed method. (C) 2023 SPIE and IS&T

关键词： oriented object detection MKIoU Loss Gaussian angle loss aerial images

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Learning Orientation-Aware Distances for oriented object detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Rao, Chaofan Wang, Jiabao Cheng, Gong Xie, Xingxing Han, Junwei Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China

oriented object detectors have suffered severely from the discontinuous boundary problem for a long time. In this work, we ingeniously avoid this problem by relating regression outputs to regression target orientations. The core idea of our method is to build a contour function which imports orientations and outputs the corresponding distance predictions. Inspired by Fourier transformations, we assume this function can be represented as a linear combination of trigonometric functions and Fourier series. We replace the final 4-D layer in the regression branch of fully convolutional one-stage object detector (FCOS) with a Fourier series transformation (FST) module and term this new network FCOSF. By this unique design, the regression outputs in FCOSF can adaptively vary according to the regression target orientations. Thus, the discontinuous boundary has no impact on our FCOSF. More importantly, FCOSF avoids building complicated oriented box representations, which usually cause extra computations and ambiguities. With only flipping augmentation and single-scale training and testing, FCOSF with ResNet-50 achieves 73.64% mean average precision (mAP) on the DOTA-v1.0 dataset with up to 23.6-frames/s speed, surpassing all one-stage oriented object detectors. On the more challenging DOTA-v2.0 dataset, FCOSF also achieves the highest results of 51.75% mAP among one-stage detectors. More experiments on DIOR-R and HRSC2016 are also conducted to verify the robustness of FCOSF. Code and models will be available at https://***/DDGRCF/FCOSF.

关键词： Detectors object detection Fourier series Feature extraction Training Predictive models Transformers Fourier series transformation (FST) orientation-aware distance oriented object detection remote sensing images

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Trigonometric-Coded Refined Detector for High Precision oriented object detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷

作者： Zhang, Rufei Wang, Yuqing Shen, Sheng Zhao, Wei Zeng, Zhiliang Li, Nannan Li, Dongjin Beijing Inst Control & Elect Technol Beijing 100038 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

oriented object detection in aerial images is a crucial link in earth observation. As a special parameter in oriented object representation, the angle is the key to achieving high-precision detection. However, the widely used regression-based methods suffer from boundary discontinuity problems due to the periodicity of the angle. To address this issue, we proposed a novel angle prediction method called fixed step trigonometric coder (FSTC). Exploiting the innate periodicity of trigonometric functions, FSTC can encode angles cyclically in a succinct, continuous, and uniform manner. Based on FSTC, we designed a single-shot oriented object detector, namely, a trigonometric-coded refined detector (TRDet), for high-precision object detection in real-time. TRDet consists of two modules: the anchor optimization module (AOM) and the object detection module (ODM). AOM employs FSTC to generate high-quality rotated anchors. In ODM, a dynamically weighted loss (DWL) was proposed to make the model focus on hard samples with higher angle deviation. Extensive experiments on DOTA and HRSC2016 show that both FSTC and TRDet can achieve competitive performance compared with peer works.

关键词： Aerial images deep learning oriented object detection

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Smooth GIoU Loss for oriented object detection in Remote Sensing Images

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REMOTE SENSING 2023年第5期15卷 1259-1259页

作者： Qian, Xiaoliang Zhang, Niannian Wang, Wei Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou Peoples R China

oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that spatial parameters are independent of one another. This independence is an ideal that is not achievable in practice. To avoid this problem, various kinds of IoU-based BBR losses have been widely used in OOD;however, their relationships with IoUs are approximately linear. Consequently, the gradient value, i.e., the learning intensity, cannot be dynamically adjusted with the IoU in these cases, which restricts the accuracy of object location. To handle this problem, a novel BBR loss, named smooth generalized intersection over union (GIoU) loss, is proposed. The contributions it makes include two aspects. First of all, smooth GIoU loss can employ more appropriate learning intensities in the different ranges of GIoU values to address the above problem and the design scheme of smooth GIoU loss can be generalized to other IoU-based BBR losses. Secondly, the existing computational scheme of GIoU loss can be modified to fit OOD. The ablation study of smooth GIoU loss validates the effectiveness of its design scheme. Comprehensive comparisons performed on two RSI datasets demonstrate that the proposed smooth GIoU loss is superior to other BBR losses adopted by existing OOD methods and can be generalized for various kinds of OOD methods. Furthermore, the core idea of smooth GIoU loss can be generalized to other IoU-based BBR losses.

关键词： oriented object detection remote sensing images smooth GIoU loss bounding box regression

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Shared-Weight-Based Multi-Dimensional Feature Alignment Network for oriented object detection in Remote Sensing Imagery

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SENSORS 2023年第1期23卷 207-207页

作者： Hu, Xinxin Zhu, Changming Shanghai Maritime Univ Coll Informat & Engn Shanghai 201306 Peoples R China

Arbitrarily oriented object detection in aerial images is a highly challenging task in computer vision. The mainstream methods are based on the feature pyramid, while for remote-sensing targets, the misalignment of multi-scale features is always a thorny problem. In this article, we address the feature misalignment problem of oriented object detection from three dimensions: spatial, axial, and semantic. First, for the spatial misalignment problem, we design an intra-level alignment network based on leading features that can synchronize the location information of different pyramid features by sparse sampling. For multi-oriented aerial targets, we propose an axially aware convolution to solve the mismatch between the traditional sampling method and the orientation of instances. With the proposed collaborative optimization strategy based on shared weights, the above two modules can achieve coarse-to-fine feature alignment in spatial and axial dimensions. Last but not least, we propose a hierarchical-wise semantic alignment network to address the semantic gap between pyramid features that can cope with remote-sensing targets at varying scales by endowing the feature map with global semantic perception across pyramid levels. Extensive experiments on several challenging aerial benchmarks show state-of-the-art accuracy and appreciable inference speed. Specifically, we achieve a mean Average Precision (mAP) of 78.11% on DOTA, 90.10% on HRSC2016, and 90.29% on UCAS-AOD.

关键词： remote-sensing images oriented object detection feature alignment anchor free convolutional neural networks

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