Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is ground...Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.展开更多
For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition t...For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition theorem. In this paper, the S-R decomposition unique existence theorem is proved, by employing matrix and tensor method. Also, a brief proof of its objectivity is given.展开更多
This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least ...This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least three mental spaces are projected by a narrative discourse, i.e., a narrated event space, a narrating space, and a basic space, and the distance between the first two spaces determines the degree of objectivity in the narrative discourse. A schema which represents the configuration of the different spaces is proposed and applied in the analysis of journalistic narratives to explore the strategies of objectivity construction. The analysis reveals that what the different journalistic narratives have in common in the construction of objectivity is to distance the narrated event space and the narrating space with the former being foregrounded in the viewpoint arrangement.展开更多
In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Paul...In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.展开更多
When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where ...When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where a scientist grappled with such an issue:the linguist Chao Yuen Ren’s application of mechanical means in his phonetic studies.In the 1920s–1930s,Chao conducted a series of field and lab studies on the dialects in southern and central China.In contrast to traditional scholars’exclusive reliance on sharp ears and rhyme books,Chao employed mechanical devices to inscribe and analyze the spectrographs of dialectical tones and used phonographs to record the articulations of his subjects.It is demonstrated that Chao’s machines not only provided a new method of observation;they also altered the theoretical understanding of certain fundamental categories in Chinese phonology,such as tones.Moreover,Chao did not aim to replace human perception with automatic mechanisms in empirical investigations.Rather,the use of machines in his research called for an active and engaged scientific persona.展开更多
The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversie...The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversies.Pandemics as extraordinary occurrences are always attractive to historians.However,infodemics and biased information threaten objective history-writing.Objectivity as it regards historians is already a much-discussed subject.In this commentary,the fundamental theories about objectivity are delineated.Second,the relationship between the infodemic and COVID-19 pandemic is explained.Lastly,the problems regarding objectivity in the historiography of the COVID-19 pandemic are explored.展开更多
China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its...China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its objectivity and manifestations in terms of pronunciation, vocabulary, syntax and text.展开更多
The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the great...The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the greater good).Because of this,scientific and medical epistemologies and praxis are often held to an esteem that is unquestioned,celebrated,and occasionally unchecked.A closer look at the history of science and medicine,however,readily reveal the extent to which the milieu of society has informed scientific and medical endeavors.As such,an understanding of how the subjectivities of scientific and medical endeavors situate within our contemporary disciplines and practices is significant to one’s ability to truly understand said disciplines.Likewise,such an evaluation will provide insight into our role in perpetuating the illusion of objectivity in these fields.With this in mind,this paper provides a philosophical and historical examination of the concept of objectivity(in contrast to subjectivity)in science and medicine.展开更多
Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the fea...Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the feasibility, objectivity, and reliability evidence for the assessment.Methods: An expert advisory group recommended a course format for the assessment that would require children to complete a series of dynamic movement skills. Criterion-referenced skill performance and completion time were the recommended forms of evaluation. Children, 8–12 years of age, self-reported their age and gender and then completed the study assessments while attending local schools or day camps. Face validity was previously established through a Delphi expert(n = 19, 21% female) review process. Convergent validity was evaluated by age and gender associations with assessment performance. Inter-and intra-rater(n = 53, 34% female) objectivity and test–retest(n = 60, 47% female) reliability were assessed through repeated test administration.Results: Median total score was 21 of 28 points(range 5–28). Median completion time was 17 s. Total scores were feasible for all 995 children who self-reported age and gender. Total score did not differ between inside and outside environments(95% confidence interval(CI) of difference:-0.7 to 0.6;p = 0.91) or with/without footwear(95%CI of difference:-2.5 to 1.9; p = 0.77). Older age(p < 0.001, η2= 0.15) and male gender(p < 0.001, η2= 0.02)were associated with a higher total score. Inter-rater objectivity evidence was excellent(intraclass correlation coefficient(ICC) = 0.99) for completion time and substantial for skill score(ICC = 0.69) for 104 attempts by 53 children(34% female). Intra-rater objectivity was moderate(ICC = 0.52) for skill score and excellent for completion time(ICC = 0.99). Reliability was excellent for completion time over a short(2–4 days; ICC = 0.84) or long(8–14days; ICC = 0.82) interval. Skill score reliability was moderate(ICC = 0.46) over a short interval, and substantial(ICC = 0.74) over a long interval.Conclusion: The Canadian Agility and Movement Skill Assessment is a feasible measure of selected fundamental, complex and combined movement skills, which are an important building block for childhood physical literacy. Moderate-to-excellent objectivity was demonstrated for children 8–12 years of age. Test–retest reliability has been established over an interval of at least 1 week. The time and skill scores can be accurately estimated by 1 trained examiner.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To addre...Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.展开更多
Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defectiv...Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defective insulator can lead to equipment breakdown,costly service interruptions,and increased maintenance demands.While unmanned aerial vehicles(UAVs)enable rapid and cost-effective collection of high-resolution imagery,accurate defect identification remains challenging due to cluttered backgrounds,variable lighting,and the diverse appearance of faults.To address these issues,we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network(HQGNN).The YOLOv10 module,fine-tuned on domainspecific UAV datasets,improves detection precision,while the HQGNN ensures multi-object tracking and temporal consistency across video frames.This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions.Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics,achieving Recall of 0.85 and Average Precision(AP)of 0.83,with clear gains in both accuracy and throughput.These advancements support automated,proactive maintenance strategies that minimize downtime and contribute to a safer,smarter energy infrastructure.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce...The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Regular detection of pavement cracks is essential for infrastructure maintenance.However,existing methods often ignore the challenges such as the continuous evolution of crack features between video frames and the dif...Regular detection of pavement cracks is essential for infrastructure maintenance.However,existing methods often ignore the challenges such as the continuous evolution of crack features between video frames and the difficulty of defect quantification.To this end,this paper proposes an integrated framework for pavement crack detection,segmentation,tracking and counting based on Transformer.Firstly,we design theVitSeg-Det network,which is an integrated detection and segmentation network that can accurately locate and segment tiny cracks in complex scenes.Second,the TransTra-Count system is developed to automatically count the number of defects by combining defect tracking with width estimation.Finally,we conduct experimental verification on three datasets.The results show that the proposed method is superior to the existing deep learning methods in detection accuracy.In addition,the actual scene video test shows that the framework can accurately label the defect location and output the number of defects in real time.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
文摘Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.
文摘For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition theorem. In this paper, the S-R decomposition unique existence theorem is proved, by employing matrix and tensor method. Also, a brief proof of its objectivity is given.
文摘This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least three mental spaces are projected by a narrative discourse, i.e., a narrated event space, a narrating space, and a basic space, and the distance between the first two spaces determines the degree of objectivity in the narrative discourse. A schema which represents the configuration of the different spaces is proposed and applied in the analysis of journalistic narratives to explore the strategies of objectivity construction. The analysis reveals that what the different journalistic narratives have in common in the construction of objectivity is to distance the narrated event space and the narrating space with the former being foregrounded in the viewpoint arrangement.
文摘In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.
文摘When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where a scientist grappled with such an issue:the linguist Chao Yuen Ren’s application of mechanical means in his phonetic studies.In the 1920s–1930s,Chao conducted a series of field and lab studies on the dialects in southern and central China.In contrast to traditional scholars’exclusive reliance on sharp ears and rhyme books,Chao employed mechanical devices to inscribe and analyze the spectrographs of dialectical tones and used phonographs to record the articulations of his subjects.It is demonstrated that Chao’s machines not only provided a new method of observation;they also altered the theoretical understanding of certain fundamental categories in Chinese phonology,such as tones.Moreover,Chao did not aim to replace human perception with automatic mechanisms in empirical investigations.Rather,the use of machines in his research called for an active and engaged scientific persona.
文摘The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversies.Pandemics as extraordinary occurrences are always attractive to historians.However,infodemics and biased information threaten objective history-writing.Objectivity as it regards historians is already a much-discussed subject.In this commentary,the fundamental theories about objectivity are delineated.Second,the relationship between the infodemic and COVID-19 pandemic is explained.Lastly,the problems regarding objectivity in the historiography of the COVID-19 pandemic are explored.
文摘China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its objectivity and manifestations in terms of pronunciation, vocabulary, syntax and text.
文摘The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the greater good).Because of this,scientific and medical epistemologies and praxis are often held to an esteem that is unquestioned,celebrated,and occasionally unchecked.A closer look at the history of science and medicine,however,readily reveal the extent to which the milieu of society has informed scientific and medical endeavors.As such,an understanding of how the subjectivities of scientific and medical endeavors situate within our contemporary disciplines and practices is significant to one’s ability to truly understand said disciplines.Likewise,such an evaluation will provide insight into our role in perpetuating the illusion of objectivity in these fields.With this in mind,this paper provides a philosophical and historical examination of the concept of objectivity(in contrast to subjectivity)in science and medicine.
基金funded by a grant from the Canadian Institutes of Health Research awarded to Dr. Meghann Lloyd and Dr. Mark Tremblay (IHD 94356)
文摘Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the feasibility, objectivity, and reliability evidence for the assessment.Methods: An expert advisory group recommended a course format for the assessment that would require children to complete a series of dynamic movement skills. Criterion-referenced skill performance and completion time were the recommended forms of evaluation. Children, 8–12 years of age, self-reported their age and gender and then completed the study assessments while attending local schools or day camps. Face validity was previously established through a Delphi expert(n = 19, 21% female) review process. Convergent validity was evaluated by age and gender associations with assessment performance. Inter-and intra-rater(n = 53, 34% female) objectivity and test–retest(n = 60, 47% female) reliability were assessed through repeated test administration.Results: Median total score was 21 of 28 points(range 5–28). Median completion time was 17 s. Total scores were feasible for all 995 children who self-reported age and gender. Total score did not differ between inside and outside environments(95% confidence interval(CI) of difference:-0.7 to 0.6;p = 0.91) or with/without footwear(95%CI of difference:-2.5 to 1.9; p = 0.77). Older age(p < 0.001, η2= 0.15) and male gender(p < 0.001, η2= 0.02)were associated with a higher total score. Inter-rater objectivity evidence was excellent(intraclass correlation coefficient(ICC) = 0.99) for completion time and substantial for skill score(ICC = 0.69) for 104 attempts by 53 children(34% female). Intra-rater objectivity was moderate(ICC = 0.52) for skill score and excellent for completion time(ICC = 0.99). Reliability was excellent for completion time over a short(2–4 days; ICC = 0.84) or long(8–14days; ICC = 0.82) interval. Skill score reliability was moderate(ICC = 0.46) over a short interval, and substantial(ICC = 0.74) over a long interval.Conclusion: The Canadian Agility and Movement Skill Assessment is a feasible measure of selected fundamental, complex and combined movement skills, which are an important building block for childhood physical literacy. Moderate-to-excellent objectivity was demonstrated for children 8–12 years of age. Test–retest reliability has been established over an interval of at least 1 week. The time and skill scores can be accurately estimated by 1 trained examiner.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.
基金supported by Ho Chi Minh City Open University,Vietnam and Suan Sunandha Rajabhat Univeristy,Thailand.
文摘Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defective insulator can lead to equipment breakdown,costly service interruptions,and increased maintenance demands.While unmanned aerial vehicles(UAVs)enable rapid and cost-effective collection of high-resolution imagery,accurate defect identification remains challenging due to cluttered backgrounds,variable lighting,and the diverse appearance of faults.To address these issues,we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network(HQGNN).The YOLOv10 module,fine-tuned on domainspecific UAV datasets,improves detection precision,while the HQGNN ensures multi-object tracking and temporal consistency across video frames.This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions.Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics,achieving Recall of 0.85 and Average Precision(AP)of 0.83,with clear gains in both accuracy and throughput.These advancements support automated,proactive maintenance strategies that minimize downtime and contribute to a safer,smarter energy infrastructure.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金supported in part by the Natural Science Foundation of Shaanxi Province of China under Grant 2024JC-YBQN-0695.
文摘Regular detection of pavement cracks is essential for infrastructure maintenance.However,existing methods often ignore the challenges such as the continuous evolution of crack features between video frames and the difficulty of defect quantification.To this end,this paper proposes an integrated framework for pavement crack detection,segmentation,tracking and counting based on Transformer.Firstly,we design theVitSeg-Det network,which is an integrated detection and segmentation network that can accurately locate and segment tiny cracks in complex scenes.Second,the TransTra-Count system is developed to automatically count the number of defects by combining defect tracking with width estimation.Finally,we conduct experimental verification on three datasets.The results show that the proposed method is superior to the existing deep learning methods in detection accuracy.In addition,the actual scene video test shows that the framework can accurately label the defect location and output the number of defects in real time.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
