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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2023年第2期4卷 93-99页

作者： Gao, Yangte Che, Zhihao Li, Lin Gao, Jianfeng Bi, Fukun Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China North China Univ Technol Sch Informat Beijing 100144 Peoples R China DFH Satellite Co LTD Remote Sensing Satellite Gen Dept Beijing 100094 Peoples R China Changfeng Elect Technol Co Ltd Remote Sensing & Telemetry Dept Lanzhou 730070 Peoples R China

object detection in aerial images has received extensive attention in the field of computer vision. Different from natural images, the aerial objects are usually distributed in any direction. Therefore, the existing detector usually needs more parameters to encode the direction information, resulting in a large number of redundant calculations. In addition, because an ordinary convolution neural network (CNN) does not effectively model the direction change, a large amount of the rotated data is required for the aerial detector. To solve these problems, we propose a deep spatial feature transformation network (DSFT-Net), which includes a spatial feature extraction module and a feature selection module. Specifically, we add the rotation convolution kernel to the detector to extract the directional feature of the rotated target to accurately predict the direction of the model. Then, we build a dual pyramid to separate the features in the classification and regression tasks. Finally, the polarization function is proposed to construct the critical features that are suitable for their respective tasks, achieving feature selection and more refined detection. Experiments on public remote sensing benchmarks (e.g., DOTA, HRSC2016, and UCAS-AOD) have proved the effectiveness of our detector.

关键词： Feature extraction Detectors Task analysis Convolution Shape Kernel Convolutional neural networks Convolutional neural network oriented object detection remote sensing images rotation alignment

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Learning Rotated Inscribed Ellipse for oriented object detection in Remote Sensing Images

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REMOTE SENSING 2021年第18期13卷 3622-3622页

作者： He, Xu Ma, Shiping He, Linyuan Ru, Le Wang, Chen Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China Northwestern Polytech Univ Unbanned Syst Res Inst Xian 710072 Peoples R China

oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.

关键词： oriented object detection rotated inscribed ellipse remote sensing images keypoint-based detection gated aggregation eccentricity-wise

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Sparse Label Assignment for oriented object detection in Aerial Images

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REMOTE SENSING 2021年第14期13卷 2664-2664页

作者： Ming, Qi Miao, Lingjuan Zhou, Zhiqiang Song, Junjie Yang, Xue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

object detection in aerial images has received extensive attention in recent years. The current mainstream anchor-based methods directly divide the training samples into positives and negatives according to the intersection-over-unit (IoU) of the preset anchors. This label assignment strategy assigns densely arranged samples for training, which leads to a suboptimal learning process and cause the model to suffer serious duplicate detections and missed detections. In this paper, we propose a sparse label assignment strategy (SLA) to select high-quality sparse anchors based on the posterior IoU of detections. In this way, the inconsistency between classification and regression is alleviated, and better performance can be achieved through balanced training. Next, to accurately detect small and densely arranged objects, we use a position-sensitive feature pyramid network (PS-FPN) with a coordinate attention module to extract position-sensitive features for accurate localization. Finally, the distance rotated IoU loss is proposed to eliminate the inconsistency between the training loss and the evaluation metric for better bounding box regression. Extensive experiments on the DOTA, HRSC2016, and UCAS-AOD datasets demonstrate the superiority of the proposed approach.

关键词： aerial image oriented object detection label assignment convolutional neural network

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Single Shot Anchor Refinement Network for oriented object detection in Optical Remote Sensing Imagery

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IEEE ACCESS 2019年 7卷 87150-87161页

作者： Bao, Songze Zhong, Xing Zhu, Ruifei Zhang, Xiaonan Li, Zhuqiang Li, Mengyang Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chang Guang Satellite Technol Co Ltd Key Lab Satellite Remote Sensing Applicat Technol Changchun 130000 Jilin Peoples R China

object detection is a challenging task in the field of remote sensing applications due to the complex backgrounds and uncertain orientation of targets. Compared with the horizontal bounding box, the oriented bounding box can provide orientation information while retaining the true size. Most existing oriented object detection methods are based on Faster-RCNN and the other one-stage methods that can achieve real-time speed but have shortcomings in localization and detection accuracy. To further enhance the performance of one-stage methods, we propose an oriented object detection framework that is based on the single shot detector, namely, single shot anchor refinement network (S(2)ARN). The S(2)ARN obtains the accurate detection results by performing two consecutive regressions. More precisely, the multilevel features of the backbone are used to regress the coordinate offsets between the predefined rotated anchors and the ground-truth boxes to generate the refined anchors. The classification and regression subnetworks assigned to the output features are used to perform the second regression to determine the class labels and further adjust the location of the refined anchors. In addition, receptive field amplification modules (RFAMs) are inserted to enlarge the receptive field and extract more discriminative features. Furthermore, in the anchor matching step, angle-related Intersection over Union (ArIoU) is used to calculate the Intersection over Union (IoU) score instead of the traditional method. Benefiting from the multiple regressions and the insensitivity of the ArIoU score to the angle deviation, the angle sampling interval of the rotated anchor can be reduced. The experimental results for the two public datasets, HRSC2016 and UCAS-AOD, demonstrate the effectiveness of the proposed network.

关键词： Convolutional neural network (CNN) remote sensing oriented object detection anchor refinement

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DETR-ORD: An Improved DETR Detector for oriented Remote Sensing object detection with Feature Reconstruction and Dynamic Query

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REMOTE SENSING 2024年第18期16卷 3516-3516页

作者： He, Xiaohai Liang, Kaiwen Zhang, Weimin Li, Fangxing Jiang, Zhou Zuo, Zhengqing Tan, Xinyan Beijing Inst Technol Sch Mechatron Engn Beijing 100811 Peoples R China Beijing Inst Technol Zhengzhou Res Inst Zhengzhou 450000 Peoples R China

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large sequences of images. Additionally, bipartite graph matching and the limit on the number of queries result in transformer-based detectors performing poorly in scenarios with multiple objects and small object sizes. We propose an improved DETR detector for oriented remote sensing object detection with Feature Reconstruction and Dynamic Query, termed DETR-ORD. It introduces rotation into the transformer architecture for oriented object detection, reduces computational cost with a hybrid encoder, and includes an IFR (image feature reconstruction) module to address the loss of positional information due to the flattening operation. It also uses ATSS to select auxiliary dynamic training queries for the decoder. This improved DETR-based detector enhances detection performance in challenging oriented optical remote sensing scenarios with similar backbone network parameters. Our approach achieves superior results on most optical remote sensing datasets, such as DOTA-v1.5 (72.07% mAP) and DIOR-R (66.60% mAP), surpassing the baseline detector.

关键词： optical remote sensing images oriented object detection transformer deep learning

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oriented-tooth recognition using a five-axis object-detection approach

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APPLIED INTELLIGENCE 2023年第9期53卷 9846-9857页

作者： Park, Jonghwan Lee, Younghoon Seoul Natl Univ Sci & Technol Dept Data Sci 232 Gongneung Ro Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Dept Ind Engn 232 Gongneung Ro Seoul 01811 South Korea

X-ray images are essential data sources for checking the condition of the teeth, gums, jaws, and bone structure of the mouth. Tooth recognition is fundamental in image-processing-based diagnoses. In most previous recognition studies, only four-axis-based object-detection models have been considered because they perform normal object detection while the object is resting on a flat surface. However, because the teeth have various orientations, the existing four-axis-based model leads to inaccurate and inefficient recognition results. Thus, in this study, we propose a five-axis-based object-detection model that considers the orientation of the tooth. Based on a tooth-image dataset labeled using the five-axis ground truth, our proposed method processed five-axis annotated data by employing a variant of the faster region-based convolutional neural network. In the experiment, our proposed method outperformed the existing four-axis approach, both qualitatively and quantitatively. The experimental results indicated that the proposed five-axis-based recognition model will be an important basis for a dental-image-based diagnosis.

关键词： Tooth recognition Five-axis recognition Recognition with orientation oriented faster R-CNN oriented object detection

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Convex-Hull Feature Adaptation for oriented and Densely Packed object detection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第8期32卷 5252-5265页

作者： Guo, Zonghao Zhang, Xiaosong Liu, Chang Ji, Xiangyang Jiao, Jianbin Ye, Qixiang Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China Tsinghua Univ Dept Automat Beijing 100190 Peoples R China

Detecting oriented and densely packed objects is a challenging problem considering that the receptive field intersection between objects causes spatial feature aliasing. In this paper, we propose a convex-hull feature adaptation (CFA) approach, with the aim to configure convolutional features in accordance with irregular object layouts. CFA roots in the convex-hull feature representation, which defines a set of dynamically sampled feature points guided by the convex intersection over union (CIoU) to bound object extent. CFA pursues optimal feature assignment by constructing convex-hull sets and iteratively splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA defines a systematic way to adapt convolutional features on regular grids to objects of irregular shapes. Experiments on DOTA and SKU110K-R datasets show that CFA achieved new state-of-the-art performance for detecting oriented and densely packed objects. CFA also sets a solid baseline for convex polygon prediction on the MS COCO dataset defined for general object detection. Code is available at https://***/SDL-GuoZonghao/BeyondBoundingBox.

关键词： Feature extraction Layout Detectors Training object detection Location awareness Transformers oriented object detection dense object detection feature adaptation convex-hull polygon detection

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RAOD: refined oriented detector with augmented feature in remote sensing images object detection

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APPLIED INTELLIGENCE 2022年第13期52卷 15278-15294页

作者： Shi, Qin Zhu, Yu Fang, Chuantao Wang, Nan Lin, Jiajun East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

object detection is a challenging task in remote sensing. Aerial images are distinguished by complex backgrounds, arbitrary orientations, and dense distributions. Considering those difficulties, this paper proposes a two-stage refined oriented detector with augmented features named RAOD. First, a novel Augmented Feature Pyramid Network (A-FPN) is built to enhance fusion both in spatial and channel dimensions. Specifically, it mainly consists of three modules: Scale Transfer Module (STM), Feature Aggregate Module (FAM) and Feature Refinement Module (FRM). STM reduces information loss when fusing features in the top-down pathway. FAM aggregates features from different scales. FRM aims to refine the integrated features using a lightweight attention module. Then, we adopt a two-step processing, which consists of a coarse stage and a refinement stage. In the coarse stage, deformable RoI pooling is adopted to improve the network's ability of modeling spatial transformations and then horizontal proposals are transformed into oriented ones. In the refinement stage, Rotated RoI align (RRoI align) is used to extract rotation-invariant features from rotated RoIs and further optimize the localization. To enhance stability and robustness during training, smooth Ln is chosen as regression loss as it has better ability in terms of robustness and stability than smooth L-1 loss. Extensive experiments on several rotation detection datasets demonstrate the effectiveness of our method. Results show that our method is able to achieve 79.78%, 74.7% and 94.82% on DOTA-v1.0, DOTA-v1.5 and HRSC2016, respectively.

关键词： Remote sensing image oriented object detection Augmented feature pyramid Deformable RoI pooling Rotated RoI align

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Framework of rod-like crops sorting based on multi-object oriented detection and analysis

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 216卷

作者： Zhou, Shuo Zhong, Ming Chai, Xiujuan Zhang, Ning Zhang, Yubin Sun, Qixin Sun, Tan Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China Minist Agr & Rural Affairs Key Lab Agr Big Data Beijing 100081 Peoples R China Harbin Inst Technol Ind Res Inst Robot & Intelligent Equipment Weihai 264209 Peoples R China Zibo Inst Digital Agr & Rural Res Zibo 255000 Peoples R China

Quality grading and sorting are crucial post-harvest procedures to increase the market value of crops. To reduce the over-dependence on manual work for these tasks, automated sorting methods have been studied. However, we find existing research focuses mainly on round-like fruits and vegetables, and most machines can only grade one object at a time in their vision pipeline. To address these limitations, this paper proposes a comprehensive framework specifically for detecting and analyzing rod-like crops based on multi-object oriented detection. Zizania shoots are primarily used to validate our methods. To implement the deep learning models, an efficient oriented bounding box label software called OBBLabel is developed, and several large-scale image datasets are constructed. Both of the software and datasets are open-sourced for the community. Based on YOLOv8 architecture, the proposed YOLO-OBB model predicts oriented bounding boxes to extract all rod-like targets in image with 0.903 mAP@0.5. A multi-label recognition model YOLO-MLD then conducts quality grading and posture perception on individual target with 93.4% mean accuracy. Thus, precise position and quality information for all objects can be obtained in near real-time for subsequent suction cup-based sorting operations. Furthermore, according to the framework, this paper designs and manufactures a prototype sorting machine with edge intelligence for rod-like crops.

关键词： Rod-like crops Quality grading Sorting machine oriented object detection Multi-label recognition

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WSODet: A Weakly Supervised oriented Detector for Aerial object detection

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

作者： Tan, Zhiwen Jiang, Zhiguo Guo, Chen Zhang, Haopeng Beihang Univ Dept Aerosp Informat Engn Image Proc Ctr Sch Astronaut Beijing Peoples R China Beihang Univ Beijing Key Lab Digital Media Beijing 102206 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simul Beijing 102206 Peoples R China

In contrast to natural objects, aerial targets are usually non-axis aligned with arbitrary orientations. However, mainstream weakly supervised object detection (WSOD) methods can only predict horizontal bounding boxes (HBBs) from existing proposals generated by offline algorithms. To predict oriented bounding boxes (OBBs) for aerial targets while testing images end-to-end without proposals, WSODet is designed leveraging on layerwise relevance propagation (LRP) and point set representation (RepPoints). To be specific, based on the mainstream WSOD framework, LRP on multiple instance learning branch (MIL-LRP) is conducted to decrease the uncertainty and ambiguity of feature map. Then, a pseudo oriented label generation algorithm is designed to obtain OBB pseudolabels, which serve as supervision to train an oriented RepPoint Net under the guidance of improved oriented loss function (IOLF). During the test, input images are sent to oriented RepPoint branch (ORB) to obtain OBB predictions without proposals. Extensive experiments on the detection in optical remote sensing images (DIOR), Northwestern Polytechnical University (NWPU) VHR-10.v2, and HRSC2016 datasets demonstrate the effectiveness of our method to predict precise oriented aerial objects, achieving 22.2%, 46.5%, and 43.3% mAP, respectively. Moreover, training jointly with ORB boosts the results of the original WSOD framework compared with the existing WSOD methods even if there is no specific design for the original structure.

关键词： Proposals object detection Uncertainty Detectors Prediction algorithms Feature extraction Convolutional neural networks Deep learning layerwise relevance propagation (LRP) oriented object detection point set representation (RepPoints) weakly supervised object detection (WSOD)

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