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A Dual-Path Multihead Feature Enhancement Detector for oriented object detection in Remote Sensing Images

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Li, Yibing Li, Zifan Ye, Fang Li, Yingsong Harbin Engn Univ Key Lab Adv Marine Commun & Informat Technol Minist Ind & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

oriented object detection in remote sensing images (RSIs) has received more and more attention due to its broader applicability in natural scenes relative to horizontal bounding boxes (HBBs). The complex scenes and multiscale targets in RSIs make it often difficult for existing studies to extract key features of the targets effectively. At the same time, due to the problem of feature inconsistency in different layers, the direct fusion of these features is likely to cause feature conflicts, resulting in degradation of detection accuracy. To solve these problems, the dual-path multihead feature enhancement detector (DP-MHFE Det), which contains two novel architectures, is proposed in this letter. The dual-path rotation feature aggregation module (DP-RFAM) improves the feature extraction capability of the network for rotating objects through dual-path structure and deformable convolution (DCN). To use these features effectively, the multihead multilevel feature fusion enhancement network (MMFFENet) is proposed to guide the feature layers to learn and retain the key features they need autonomously, and then enhance their features according to the characteristics of different subtasks. Experiments conducted on two remote sensing datasets, DOTA and HRSC2016, show that DP-MHFE Det is faster than almost all detection methods compared to the state-of-the-art (SOTA) methods while showing strong competitiveness in accuracy.

关键词： Feature extraction Task analysis Convolution object detection Detectors Training Remote sensing Dual-path feature enhancement multihead oriented object detection remote sensing image (RSI)

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Semantic-Edge-Supervised Single-Stage Detector for oriented object detection in Remote Sensing Imagery

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REMOTE SENSING 2022年第15期14卷 3637-3637页

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

In recent years, significant progress has been made in arbitrary-oriented object detection. Different from natural images, object detection in aerial images remains its problems and challenges. Current feature enhancement strategies in this field mainly focus on enhancing the local critical response of the target while ignoring the target's contextual information, which is indispensable for detecting remote sensing targets in complex backgrounds. In this paper, we innovatively combine semantic edge detection with arbitrary-oriented object detection and propose a feature enhancement network base on a semantic edge supervision module (SES) that realizes an attention-like mechanism in three dimensions of space, channel, and pyramid level. It helps the network pay attention to the edge features of targets at multiple scales to obtain more regression clues. Furthermore, to solve the problem of dense objects with different directions in remote sensing images, we propose a rotation-invariant spatial pooling pyramid (RISPP) to extract the features of objects from multiple orientations. Based on the two feature enhancement modules, we named the network SE2-Det;extensive experiments on large public datasets of aerial images (DOTA and UCAS-AOD) validate our approach's effectiveness and demonstrate our detector's superior performance.

关键词： aerial images oriented object detection feature enhancement convolutional neural network

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Attitude Estimation and Geometry Inversion of Satellite Based on oriented object detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Xie, Pengfei Zhang, Lei Ma, Yan Zhou, Yejian Wang, Xiaoqing Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Beijing Inst Tracking Telementry & Telecommun Beijing 100094 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

Retrieval of attitude and geometry of satellite targets from inverse synthetic aperture radar (ISAR) images is an important but difficult task, because of the complex scattering phenomenon and motion-dependent projection mechanism. In this letter, we construct a novel component extraction network (CEN)-based oriented object detection to obtain the projection parameters of the target components from ISAR images, then combine this CEN with particle swarm optimization (PSO) algorithm to retrieve the 3-D attitude and geometry of the target components. This proposed method can be used to accurately estimate the attitude and geometry of the target. The simulation experiments show the effectiveness and superiority of this method.

关键词： Feature extraction Geometry Satellites Monitoring Position measurement object detection Imaging Attitude determination component extraction geometry inversion inverse synthetic aperture radar (ISAR) image oriented object detection

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Short-Side Excursion for oriented object detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Cheng, Yuhu Xu, Chengqing Kong, Yi Wang, Xuesong China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

oriented object detection has achieved a significant progress in image processing. Compared with horizontal detection methods, oriented detectors add the orientation parameter in regression to locate objects. However, the existing rotation and quadrilateral representations are not appropriate for oriented two-stage methods to generate efficient oriented proposals. In this letter, we propose a novel framework to detect oriented objects, termed short-side excursion detection (SSEDet). Inspired by the circle theorem, we propose a transformation method from horizontal rectangles to oriented ones to accurately describe oriented objects. To be specific, we exploit the offset of short sides relative to the top-right vertex to represent the orientation of rectangle. Compared with the horizontal rectangle, the representation parameters of oriented rectangle have only one more orientation parameter. Under the action of the orientation parameter, the one-to-one correspondence between representation parameters and oriented rectangle can be realized. Experimental results on commonly used datasets verify that the SSEDet can generate high-quality oriented proposals.

关键词： Proposals Feature extraction object detection Detectors Head Rotation measurement Remote sensing Circle theorem oriented object detection oriented rectangle short-side excursion (SSE)

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Hybrid Network Model: TransConvNet for oriented object detection in Remote Sensing Images

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REMOTE SENSING 2022年第9期14卷 2090页

作者： Liu, Xulun Ma, Shiping He, Linyuan Wang, Chen Chen, Zhe Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China Northwestern Polytech Univ Unbanned Syst Res Inst Xian 710072 Peoples R China Xian Univ Posts & Telecommun Natl Engn Lab Wireless Secur Xian 710121 Peoples R China

The complexity of backgrounds, the diversity of object scale and orientation, and the defects of convolutional neural network (CNN) have always been the challenges of oriented object detection in remote sensing images (RSIs). This paper designs a hybrid network model to meet these challenges and further improve the effect of oriented object detection. The inductive bias of CNN makes the network translation invariant, but it is difficult to adapt to RSIs with arbitrary object direction. Therefore, this paper designs a hybrid network, TransConvNet, which integrates the advantages of CNN and self-attention-based network, pays more attention to the aggregation of global and local information, makes up for the lack of rotation invariability of CNN with strong contextual attention, and adapts to the arbitrariness of the object direction of RSIs. In addition, to resolve the influence of complex backgrounds and multi-scale, an adaptive feature fusion network (AFFN) is designed to improve the information representation ability of feature maps with different resolutions. Finally, the adaptive weight loss function is used to train the network to further improve the effect of object detection. Extensive experimental results on the DOTA, UCASAOD, and VEDAI data sets demonstrate the effectiveness of the proposed method.

关键词： oriented object detection remote sensing images self-attention transformer feature fusion

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MSCNet: A Multilevel Stacked Context Network for oriented object detection in Optical Remote Sensing Images

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REMOTE SENSING 2022年第20期14卷 5066-5066页

作者： Zhang, Rui Zhang, Xinxin Zheng, Yuchao Wang, Dahan Hua, Lizhong Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361024 Peoples R China Xiamen Univ Technol Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

oriented object detection has recently become a hot research topic in remote sensing because it provides a better spatial expression of oriented target objects. Although research has made considerable progress in this field, the feature of multiscale and arbitrary directions still poses great challenges for oriented object detection tasks. In this paper, a multilevel stacked context network (MSCNet) is proposed to enhance target detection accuracy by aggregating the semantic relationships between different objects and contexts in remote sensing images. Additionally, to alleviate the impact of the defects of the traditional oriented bounding box representation, the feasibility of using a Gaussian distribution instead of the traditional representation is discussed in this paper. Finally, we verified the performance of our work on two common remote sensing datasets, and the results show that our proposed network improved on the baseline.

关键词： oriented object detection multilevel stacked context remote sensing images

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Polar Ray: A Single-stage Angle-free Detector for oriented object detection in Aerial Images 21

Polar Ray: A Single-stage Angle-free Detector for Oriented O...

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29th ACM International Conference on Multimedia (MM)

作者： Liu, Shuai Zhang, Lu Hao, Shuai Lu, Huchuan He, You Dalian Univ Technol Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Naval Aeronaut Univ Yantai Peoples R China

ISBN: (纸本)9781450386517

oriented bounding boxes are widely used for object detection in aerial images. Existing oriented object detection methods typically follow the general object detection paradigm by adding an extra rotation angle on the horizontal bounding boxes. However, the angular periodicity incurs the difficulty in angle regression and rotation sensitivity on bounding boxes. In this paper, we propose a new anchor-free oriented object detector, Polar Ray Network (PRNet), where object keypoints are represented by polar coordinates without angle regression. Our PRNet learns a set of polar rays from the object center to boundary with predefined equal-distributed angles. We introduce a dynamic PointConv module to optimize the regression of polar ray by incorporating object corner features. Furthermore, a classification feature guidance module is presented to improve the classification accuracy by incorporating more spatial contents from polar rays. Experimental results on two public datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed PRNet significantly outperforms existing anchor-free detectors, and shows highly competitiveness with the state-of-the-art two-stage anchor-based methods.

关键词： oriented object detection Polar rays Rotation angle regression free

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Center-Boundary Dual Attention for oriented object detection in Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Shuai Zhang, Lu Lu, Huchuan He, You Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Naval Aeronaut Univ Inst Informat Fus Yantai 264001 Peoples R China Dalian Univ Technol Dept Elect Engn Dalian 116024 Peoples R China

Recently, anchor-free object detectors have shown promising performance in oriented object detection on remote sensing images. However, the objects in remote sensing images always have large variations in arbitrary orientations, sizes, and aspect ratios, which makes the existing anchor-free methods hard to obtain satisfactory results. In this article, we propose a novel anchor-free detector, center-boundary dual attention (CBDA) network (CBDA-Net), for fast and accurate oriented object detection on remote sensing images. In CBDA-Net, we construct a CBDA module, which utilizes a dual attention mechanism to extract attention features on the center and boundary regions of objects. The CBDA module can learn more essential features for rotating objects and reduce the interference from complex background. Besides, to resolve the influence of object aspect ratio on angle errors, we propose an aspect ratio weighted angle loss (arwLoss), where diffident penalties are assigned on the angle loss based on the aspect ratios of objects. This loss construction is effective in improving the detection accuracy of oriented objects, especially for slender objects. We conduct extensive experiments on two publish benchmarks, i.e., DOTA and HRSC2016. The experimental results demonstrate that our CBDA-Net achieves favorable performance against other anchor-free state of the arts with a real-time speed of 50 FPS.

关键词： object detection Remote sensing Feature extraction Detectors Task analysis Sensors Sensitivity Anchor-free dual attention oriented object detection remote sensing images

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Precise maize detasseling base on oriented object detection for tassels

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 202卷

作者： Song, Chaoyu Zhang, Fan Li, Jiansheng Zhang, Junxiong China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Coll Agron & Biotechnol Beijing 100083 Peoples R China

The maize detasseling process was gradually automated with the continuous promotion of the maize detasseling machine. Nevertheless, some problems are gradually becoming more prominent for it. Most maize detasseling machine has a simple maize tassels detection system. That is, the height of the crop was identified, and not the specific information on the maize tassels, leading to low detasseling precision and a high leaf injury rate during maize detasseling machine working. Aiming at these issues, this paper proposed a novel method for the maize tassel pose estimation based on computer vision and oriented object detection. Specifically, a two-step frame-work for maize tassel pose estimation is developed. Firstly, the maize plant is captured from above, then the maize tassels and the second leaf's vein are posed in the horizontal plane, and their poses are matched to the oriented bounding box. Second, an oriented bounding box is generated according to the oriented R-CNN model detection. Since maize tassels in the field differ in size and the morphological color of different growth stages, estimating the maize tassel pose accurately by the angle of the oriented bounding box alone is unfeasible. Therefore, these pixels of the oriented bounding box were put into the Look Twice module to extract the critical information about maize tassels, which can accurately determine the final pose of the maize tassels. Finally, evaluation metrics on the test set indicate the proposed method performed with correct maize tassels pose estimation rate of 88.56% and 29.57 Giga Floating-point Operations (GFLOPs), which indicated that the feasi-bility of maize tassel poses estimation using the proposed method. This study provides the possibility and foundation for precise maize detasseling in maize detasseling machines.

关键词： Maize Maize tassels oriented object detection Detasseling Deep learning

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FAR-Net: Fast Anchor Refining for Arbitrary-oriented object detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Deng, Chenwei Jing, Donglin Han, Yuqi Wang, Shuliang Wang, Hongshuo Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Beijing 100854 Peoples R China

Compared with natural images, targets in remote-sensing images are often distributed with more flexible orientation, aspect ratio, and scale. Thus, anchor-based algorithms often employ plenty of preset anchors to encode the above-mentioned attributes in object detection tasks. However, they often suffer from the following issues: 1) significant computational burden caused by dense-sampling anchors;2) serious background interference since many anchors only cover small parts of the actual target;and 3) feature misalignment between the targets with the preset anchors due to the absence of the most discriminant features for target extraction. Therefore, in this letter, a fast anchor refining network (FAR-Net) is advocated to address the remaining issues for arbitrary-oriented object detection in the remote-sensing field. To be specific, a rotation alignment module (RAM) and balanced regression loss function (BR-loss) are carefully designed in the FAR-Net. The RAM is capable of generating high-quality anchors based on a refinement convolution and adaptively aligning the convolutional features by complying with the anchor boxes to reduce redundant calculation. The BR-loss is designed by employing a balanced loss function to prevent misaligned anchors from causing major gradient descents, thereby achieving a more stable network training procedure. Extensive experiments on public remote-sensing datasets (HRSC2016 and UCAS-AOD) demonstrate the excellent detection performance of our algorithm in comparison with numerous existing detectors.

关键词： Remote sensing Random access memory Training object detection Feature extraction Task analysis Sensors Convolutional neural network (CNN) oriented object detection remote-sensing images rotation alignment

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