The basic scheme of the orientation detection system using L-shape reticle is introduced. The dimension of the patterns on the reticle of the system in practical applications is designed and an analysis of the princip...The basic scheme of the orientation detection system using L-shape reticle is introduced. The dimension of the patterns on the reticle of the system in practical applications is designed and an analysis of the principle of abstracting the orientation information of the target and the effects and formation method of self-adapting tracking gate is presented. The research result shows that the orientation detection system using L-shape reticle has a good effect on space-filtering, the signals that the orientation detection system sends out are easy to be processed by computer, its self-adapting tracking gate has a strong anti-interference ability, and the whole system＇s searching and tracking performances are quite high.展开更多
Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current frameworks for oriented detection modules are co...Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current 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 inconsistencies.To 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 detection.The 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 efficiency.These improvements empower the model to accurately detect objects across varying scales.The ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box coherence.This approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection accuracy.Comprehensive 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 models.These findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.展开更多
The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orient...The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.展开更多
Due to the bird’s eye view of remote sensing sensors,the orientational information of an object is a key factor that has to be considered in object detection.To obtain rotating bounding boxes,existing studies either ...Due to the bird’s eye view of remote sensing sensors,the orientational information of an object is a key factor that has to be considered in object detection.To obtain rotating bounding boxes,existing studies either rely on rotated anchoring schemes or adding complex rotating ROI transfer layers,leading to increased computational demand and reduced detection speeds.In this study,we propose a novel internal-external optimized convolutional neural network for arbitrary orientated object detection in optical remote sensing images.For the internal opti-mization,we designed an anchor-based single-shot head detector that adopts the concept of coarse-to-fine detection for two-stage object detection networks.The refined rotating anchors are generated from the coarse detection head module and fed into the refining detection head module with a link of an embedded deformable convolutional layer.For the external optimiza-tion,we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes.Experimental results on the DOTA and HRSC2016 bench-mark datasets show that our proposed method outperforms selected methods.展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
Rail positioning is a critical step for detecting rail defects downstream.However,existing orientation-based detectors struggle to effectively manage rails with arbitrary inclinations and high aspect ratios,particular...Rail positioning is a critical step for detecting rail defects downstream.However,existing orientation-based detectors struggle to effectively manage rails with arbitrary inclinations and high aspect ratios,particularly in turnout sections.To address these challenges,a fuzzy boundary guidance and oriented Gaussian function-based anchor-free network termed the rail positioning network(RP-Net)is proposed for rail positioning in turnout sections.First,an oriented Gaussian function-based label generation strategy is introduced.This strategy produces smoother and more accu-rate label values by accounting for the specific aspect ratios and orientations of the rails.Second,a fuzzy boundary learning module is developed to enhance the network’s abil-ity to model the rail boundary regions effectively.Further-more,a boundary guidance module is developed to direct the network in fusing the features obtained from the downs-ampled network output with the boundary region features,which have been enhanced to contain more refined posi-tional and structural information.A local channel attention mechanism is integrated into this module to identify critical channels.Finally,experiments conducted on the tracking dataset show that the proposed RP-Net achieves high posi-tioning accuracy and demonstrates strong adaptability in complex scenarios.展开更多
High-throughput sequencing has identified a large number of sense-antisense transcriptional pairs, which indicates that these genes were transcribed from both directions. Recent reports have demonstrated that many ant...High-throughput sequencing has identified a large number of sense-antisense transcriptional pairs, which indicates that these genes were transcribed from both directions. Recent reports have demonstrated that many antisense RNAs, especially lnc RNA（long non-coding RNA）, can interact with the sense RNA by forming an RNA duplex. Many methods, such as RNA-sequencing, Northern blotting, RNase protection assays and strand-specific PCR, can be used to detect the antisense transcript and gene transcriptional orientation. However, the applications of these methods have been constrained, to some extent, because of the high cost, difficult operation or inaccuracy, especially regarding the analysis of substantial amounts of data. Thus, we developed an easy method to detect and validate these complicated RNAs. We primarily took advantage of the strand specificity of RT-PCR and the single-strand specificity of S1 endonuclease to analyze sense and antisense transcripts. Four known genes, including mouse β-actin and Tsix（Xist antisense RNA）, chicken LXN（latexin） and GFM1（Gelongation factor, mitochondrial 1）, were used to establish the method. These four genes were well studied and transcribed from positive strand, negative strand or both strands of DNA, respectively, which represented all possible cases. The results indicated that the method can easily distinguish sense, antisense and sense-antisense transcriptional pairs. In addition, it can be used to verify the results of high-throughput sequencing, as well as to analyze the regulatory mechanisms between RNAs. This method can improve the accuracy of detection and can be mainly used in analyzing single gene and was low cost.展开更多
Interaction of straight chain alcohol vapors with MOF-199-functionalized films was studied by SPR. The signals had linear relationships with the concentration of alcohols over a wide range from 0 to 70% （v/v） and we...Interaction of straight chain alcohol vapors with MOF-199-functionalized films was studied by SPR. The signals had linear relationships with the concentration of alcohols over a wide range from 0 to 70% （v/v） and were reversible in proportional to the chain length, with R2 all above 0.99.展开更多
Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered o...Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.展开更多
Transmission towers play a crucial role in overhead transmission line systems and are the key target of transmission line inspections.With the help of remote sensing technology,transmission towers can be effectively d...Transmission towers play a crucial role in overhead transmission line systems and are the key target of transmission line inspections.With the help of remote sensing technology,transmission towers can be effectively detected in wide areas at reasonable costs and in a relatively short time period.However,it is difficult to identify the type of transmission towers in optical remote sensing images due to detail degradation caused by long-distance and high-altitude imaging.This paper proposes a transmission tower detection method in optical remote sensing images using an oriented object detector and object and shadow joint detection.To enrich the information,the transmission towers and their shadows are jointly detected through a CenterNet detector with an orientation prediction branch.To improve the detection accuracy of difficult objects,attention and deformable convolutional network modules are introduced to the backbone and orientation prediction branches,respectively.Considering the orientation and the aspect ratio of the objects and shadows,a focal loss function with an aspect ratio is employed to further improve the accuracy.Object and shadow joint detection are separately realized through the one-box and multi-box detection strategies.A transmission tower dataset RSITT labeled with horizontal and oriented boxes is established.Experiments conducted on the RSITT dataset have demonstrated that the detection accuracy and recall rate of the proposed joint detection algorithm reached 73.2%and 95.2%.展开更多
Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or mis...Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.展开更多
文摘The basic scheme of the orientation detection system using L-shape reticle is introduced. The dimension of the patterns on the reticle of the system in practical applications is designed and an analysis of the principle of abstracting the orientation information of the target and the effects and formation method of self-adapting tracking gate is presented. The research result shows that the orientation detection system using L-shape reticle has a good effect on space-filtering, the signals that the orientation detection system sends out are easy to be processed by computer, its self-adapting tracking gate has a strong anti-interference ability, and the whole system＇s searching and tracking performances are quite high.
基金supported by the Key Research and Development Program of Shaanxi Province(2024GX-YBXM-010).
文摘Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current 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 inconsistencies.To 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 detection.The 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 efficiency.These improvements empower the model to accurately detect objects across varying scales.The ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box coherence.This approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection accuracy.Comprehensive 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 models.These findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.
文摘The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.
基金This work is supported by the National Natural Science Foundation of China[grant numbers 41890820,41771452,41771454,and 41901340]。
文摘Due to the bird’s eye view of remote sensing sensors,the orientational information of an object is a key factor that has to be considered in object detection.To obtain rotating bounding boxes,existing studies either rely on rotated anchoring schemes or adding complex rotating ROI transfer layers,leading to increased computational demand and reduced detection speeds.In this study,we propose a novel internal-external optimized convolutional neural network for arbitrary orientated object detection in optical remote sensing images.For the internal opti-mization,we designed an anchor-based single-shot head detector that adopts the concept of coarse-to-fine detection for two-stage object detection networks.The refined rotating anchors are generated from the coarse detection head module and fed into the refining detection head module with a link of an embedded deformable convolutional layer.For the external optimiza-tion,we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes.Experimental results on the DOTA and HRSC2016 bench-mark datasets show that our proposed method outperforms selected methods.
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
基金Major Scientific Research Projects of China Railway Group(No.K2019G046)the National Key Research and Devel-opment Program of China(No.2020YFB1600700).
文摘Rail positioning is a critical step for detecting rail defects downstream.However,existing orientation-based detectors struggle to effectively manage rails with arbitrary inclinations and high aspect ratios,particularly in turnout sections.To address these challenges,a fuzzy boundary guidance and oriented Gaussian function-based anchor-free network termed the rail positioning network(RP-Net)is proposed for rail positioning in turnout sections.First,an oriented Gaussian function-based label generation strategy is introduced.This strategy produces smoother and more accu-rate label values by accounting for the specific aspect ratios and orientations of the rails.Second,a fuzzy boundary learning module is developed to enhance the network’s abil-ity to model the rail boundary regions effectively.Further-more,a boundary guidance module is developed to direct the network in fusing the features obtained from the downs-ampled network output with the boundary region features,which have been enhanced to contain more refined posi-tional and structural information.A local channel attention mechanism is integrated into this module to identify critical channels.Finally,experiments conducted on the tracking dataset show that the proposed RP-Net achieves high posi-tioning accuracy and demonstrates strong adaptability in complex scenarios.
基金supported by the National Natural Science Foundation of China(31301958)the Chinese Postdoctoral Science Foundation(2013T60808)
文摘High-throughput sequencing has identified a large number of sense-antisense transcriptional pairs, which indicates that these genes were transcribed from both directions. Recent reports have demonstrated that many antisense RNAs, especially lnc RNA（long non-coding RNA）, can interact with the sense RNA by forming an RNA duplex. Many methods, such as RNA-sequencing, Northern blotting, RNase protection assays and strand-specific PCR, can be used to detect the antisense transcript and gene transcriptional orientation. However, the applications of these methods have been constrained, to some extent, because of the high cost, difficult operation or inaccuracy, especially regarding the analysis of substantial amounts of data. Thus, we developed an easy method to detect and validate these complicated RNAs. We primarily took advantage of the strand specificity of RT-PCR and the single-strand specificity of S1 endonuclease to analyze sense and antisense transcripts. Four known genes, including mouse β-actin and Tsix（Xist antisense RNA）, chicken LXN（latexin） and GFM1（Gelongation factor, mitochondrial 1）, were used to establish the method. These four genes were well studied and transcribed from positive strand, negative strand or both strands of DNA, respectively, which represented all possible cases. The results indicated that the method can easily distinguish sense, antisense and sense-antisense transcriptional pairs. In addition, it can be used to verify the results of high-throughput sequencing, as well as to analyze the regulatory mechanisms between RNAs. This method can improve the accuracy of detection and can be mainly used in analyzing single gene and was low cost.
基金supported by NSFC(Nos.21027003, 21235007 and 91117010)Ministry of Science and Technology(No. 2012IM030400) and Chinese Academy of Sciences
文摘Interaction of straight chain alcohol vapors with MOF-199-functionalized films was studied by SPR. The signals had linear relationships with the concentration of alcohols over a wide range from 0 to 70% （v/v） and were reversible in proportional to the chain length, with R2 all above 0.99.
文摘Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.
基金supported by the National Key R&D Program of China(2020YFB0905900).
文摘Transmission towers play a crucial role in overhead transmission line systems and are the key target of transmission line inspections.With the help of remote sensing technology,transmission towers can be effectively detected in wide areas at reasonable costs and in a relatively short time period.However,it is difficult to identify the type of transmission towers in optical remote sensing images due to detail degradation caused by long-distance and high-altitude imaging.This paper proposes a transmission tower detection method in optical remote sensing images using an oriented object detector and object and shadow joint detection.To enrich the information,the transmission towers and their shadows are jointly detected through a CenterNet detector with an orientation prediction branch.To improve the detection accuracy of difficult objects,attention and deformable convolutional network modules are introduced to the backbone and orientation prediction branches,respectively.Considering the orientation and the aspect ratio of the objects and shadows,a focal loss function with an aspect ratio is employed to further improve the accuracy.Object and shadow joint detection are separately realized through the one-box and multi-box detection strategies.A transmission tower dataset RSITT labeled with horizontal and oriented boxes is established.Experiments conducted on the RSITT dataset have demonstrated that the detection accuracy and recall rate of the proposed joint detection algorithm reached 73.2%and 95.2%.
文摘Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.
