Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by ...Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.展开更多
The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations...The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.展开更多
The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integratio...The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.展开更多
Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system ...Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system for these patients. Methods: Ninety patients with type 2 diabetes admitted to the Department of Endocrinology of the hospital from January 2024 to June 2024 were selected. The control group (n = 45) received routine nursing care, while the observation group (n = 45) received whole-course nursing. Indicators such as glucose metabolism and compliance behavior were measured before and after care, and the health and quality of life of patients in both groups were evaluated. Results: A comparison of blood glucose levels and compliance behavior showed that the observation group had lower blood glucose levels than the control group (P < 0.05). Additionally, the compliance behavior score of the observation group was higher than that of the control group (P < 0.05). Conclusion: The holistic nursing model demonstrates significant nursing effects for patients with type 2 diabetes. This approach not only assists in blood sugar control, prevents disease progression, and reduces complications, but also enhances patients’ knowledge of health management, aiding in their recovery.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, an...In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.展开更多
This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line ...This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line strip laser and one color CCD camera. The scanned object is pictured twice by a color CCD camera. First, the texture of the scanned object is taken by a color CCD camera. Then the 3D information of the scanned object is obtained from laser plane equations. This paper presents a practical way to implement the three dimensional measuring method and the automatic registration of a large 3D object and a pretty good result is obtained after experiment verification.展开更多
The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficien...The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.展开更多
Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew back...Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.展开更多
There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most ...There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.展开更多
A SOTER management system was developed by analyzing, designing, programming, testing, repeated proceeding and progressing based on the object-oriented method. The function of the attribute database management is inhe...A SOTER management system was developed by analyzing, designing, programming, testing, repeated proceeding and progressing based on the object-oriented method. The function of the attribute database management is inherited and expanded in the new system. The integrity and security of the SOTER database are enhanced. The attribute database management, the spatial database management and the model base are integrated into SOTER based on the component object model (COM), and the graphical user interface (GUI) for Windows is used to interact with clients, thus being easy to create and maintain the SOTER, and convenient to promote the quantification and automation of soil information application.展开更多
An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method...An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.展开更多
It is difficult to parallelize a subsistent sequential algorithm. Through analyzing the sequential algorithm of a Global Atmospheric Data Objective Analysis System, this article puts forward a distributed parallel alg...It is difficult to parallelize a subsistent sequential algorithm. Through analyzing the sequential algorithm of a Global Atmospheric Data Objective Analysis System, this article puts forward a distributed parallel algorithm that statically distributes data on a massively parallel processing (MPP) computer. The algorithm realizes distributed parailelization by extracting the analysis boxes and model grid point Iatitude rows with leaped steps, and by distributing the data to different processors. The parallel algorithm achieves good load balancing, high parallel efficiency, and low parallel cost. Performance experiments on a MPP computer arc also presented.展开更多
A class of large-scale hierarchical control systems is considered, the overall objective function is a nonlinear and nonseparable function of multiple quadratic performance indices.The separation strategy of the multi...A class of large-scale hierarchical control systems is considered, the overall objective function is a nonlinear and nonseparable function of multiple quadratic performance indices.The separation strategy of the multiobjective optimization technique and the three-level objective coordination method are applied to the large -sacle systems, and a four-level hierarchical algorithms of optimization control is obtained.展开更多
Drone also known as unmanned aerial vehicle(UAV)has drawn lots of attention in recent years.Quadcopter as one of the most popular drones has great potential in both industrial and academic fields.Quadcopter drones are...Drone also known as unmanned aerial vehicle(UAV)has drawn lots of attention in recent years.Quadcopter as one of the most popular drones has great potential in both industrial and academic fields.Quadcopter drones are capable of taking off vertically and flying towards any direction.Traditional researches of drones mainly focus on their mechanical structures and movement control.The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms.Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission.Implementing different functions on mobile devices can result in different behaviors of drones indirectly.With the development of deep learning in computer vision field,commercial drones equipped with camera can be much more intelligent and even realize autonomous flight.In the past,running deep learning based algorithms on mobile devices is highly computational intensive and time consuming.This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.展开更多
The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functio...The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functions for the vibration design of a pipeline or pipe system are introduced,namely,the frequency,amplitude,transfer ratio,curvature and deformation energy as options for the optimization process.The genetic algorithms(GA)are adopted as the opti- mization method,in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial.The optimization procedure for all the above ob- jective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN. The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example,and their characteristics are analyzed.Finally we put forward two new objective functions,curvature and deformation energy for pipe system optimization.The calculations show that using the curvature as the objective function can reflect the case of minimal stress,and the optimization results using the deformation energy represent lesser and more uni- form stress distribution.The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement.展开更多
BACKGROUND System based practice(SBP) milestones require trainees to effectively navigate the larger health care system for optimal patient care. In gastroenterology training programs, the assessment of SBP is difficu...BACKGROUND System based practice(SBP) milestones require trainees to effectively navigate the larger health care system for optimal patient care. In gastroenterology training programs, the assessment of SBP is difficult due to high volume, high acuity inpatient care, as well as inconsistent direct supervision. Nevertheless,structured assessment is required for training programs. We hypothesized that objective structured clinical examination(OSCE) would be an effective tool for assessment of SBP.AIM To develop a novel method for SBP milestone assessment of gastroenterology fellows using the OSCE.METHODS For this observational study, we created 4 OSCE stations: Counseling an impaired colleague, handoff after overnight call, a feeding tube placement discussion, and giving feedback to a medical student on a progress note. Twentysix first year fellows from 7 programs participated. All fellows encountered identical case presentations. Checklists were completed by trained standardized patients who interacted with each fellow participant. A report with individual and composite scores was generated and forwarded to program directors to utilize in formative assessment. Fellows also received immediate feedback from a faculty observer and completed a post-session program evaluation survey.RESULTS Survey response rate was 100%. The average composite score across SBP milestones for all cases were 6.22(SBP1), 4.34(SBP2), 3.35(SBP3), and 6.42(SBP4)out of 9. The lowest composite score was in SBP 3, which asks fellows to advocate for cost effective care. This highest score was in patient care 2, which asks fellows to develop comprehensive management plans. Discrepancies were identified between the fellows’ perceived performance in their self-assessments and Standardized Patient checklist evaluations for each case. Eighty-seven percent of fellows agreed that OSCEs are an important component of their clinical training,and 83% stated that the cases were similar to actual clinical encounters. All participating fellows stated that the immediate feedback was "very useful." One hundred percent of the fellows stated they would incorporate OSCE learning into their clinical practice.CONCLUSION OSCEs may be used for standardized evaluation of SBP milestones. Trainees scored lower on SBP milestones than other more concrete milestones. Training programs should consider OSCEs for assessment of SBP.展开更多
文摘Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金the Scientific Research Project of Education Department of Hunan Province of China under grant number 22B0549International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Province Undergraduate Innovation and Entrepreneurship Training Program(grant number S202410531015).
文摘The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.
文摘The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.
文摘Objective: To explore the effect of a whole-course nursing objective management system on disease control and quality of life in patients with type 2 diabetes, and to propose strategies for constructing such a system for these patients. Methods: Ninety patients with type 2 diabetes admitted to the Department of Endocrinology of the hospital from January 2024 to June 2024 were selected. The control group (n = 45) received routine nursing care, while the observation group (n = 45) received whole-course nursing. Indicators such as glucose metabolism and compliance behavior were measured before and after care, and the health and quality of life of patients in both groups were evaluated. Results: A comparison of blood glucose levels and compliance behavior showed that the observation group had lower blood glucose levels than the control group (P < 0.05). Additionally, the compliance behavior score of the observation group was higher than that of the control group (P < 0.05). Conclusion: The holistic nursing model demonstrates significant nursing effects for patients with type 2 diabetes. This approach not only assists in blood sugar control, prevents disease progression, and reduces complications, but also enhances patients’ knowledge of health management, aiding in their recovery.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金National Science and Technology Council,the Republic of China,under grants NSTC 113-2221-E-194-011-MY3 and Research Center on Artificial Intelligence and Sustainability,National Chung Cheng University under the research project grant titled“Generative Digital Twin System Design for Sustainable Smart City Development in Taiwan.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
文摘In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.
文摘This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line strip laser and one color CCD camera. The scanned object is pictured twice by a color CCD camera. First, the texture of the scanned object is taken by a color CCD camera. Then the 3D information of the scanned object is obtained from laser plane equations. This paper presents a practical way to implement the three dimensional measuring method and the automatic registration of a large 3D object and a pretty good result is obtained after experiment verification.
文摘The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.
基金This project was supported by the foundation of the Visual and Auditory Information Processing Laboratory of BeijingUniversity of China (0306) and the National Science Foundation of China (60374031).
文摘Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.
基金supported in part by the Institute for Guo Qiang of Tsinghua University(2019GQG1023)in part by Graduate Education and Teaching Reform Project of Tsinghua University(202007J007)+1 种基金in part by National Natural Science Foundation of China(U19B2029,62073028,61803222)in part by the Independent Research Program of Tsinghua University(2018Z05JDX002)。
文摘There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.
基金Project supported by the National Natural Science Foundation of China (No. 40271056) Hubei Provin- cial Natural Science Foundation of China (No. 99J123).
文摘A SOTER management system was developed by analyzing, designing, programming, testing, repeated proceeding and progressing based on the object-oriented method. The function of the attribute database management is inherited and expanded in the new system. The integrity and security of the SOTER database are enhanced. The attribute database management, the spatial database management and the model base are integrated into SOTER based on the component object model (COM), and the graphical user interface (GUI) for Windows is used to interact with clients, thus being easy to create and maintain the SOTER, and convenient to promote the quantification and automation of soil information application.
基金supported by the National Natural Science Foundation of China(60835004 60775047+2 种基金 60872130)the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)
文摘An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.
文摘It is difficult to parallelize a subsistent sequential algorithm. Through analyzing the sequential algorithm of a Global Atmospheric Data Objective Analysis System, this article puts forward a distributed parallel algorithm that statically distributes data on a massively parallel processing (MPP) computer. The algorithm realizes distributed parailelization by extracting the analysis boxes and model grid point Iatitude rows with leaped steps, and by distributing the data to different processors. The parallel algorithm achieves good load balancing, high parallel efficiency, and low parallel cost. Performance experiments on a MPP computer arc also presented.
文摘A class of large-scale hierarchical control systems is considered, the overall objective function is a nonlinear and nonseparable function of multiple quadratic performance indices.The separation strategy of the multiobjective optimization technique and the three-level objective coordination method are applied to the large -sacle systems, and a four-level hierarchical algorithms of optimization control is obtained.
基金This work is supported by the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grant U1836208,U1536206,U1836110,61602253,61672294+2 种基金by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China。
文摘Drone also known as unmanned aerial vehicle(UAV)has drawn lots of attention in recent years.Quadcopter as one of the most popular drones has great potential in both industrial and academic fields.Quadcopter drones are capable of taking off vertically and flying towards any direction.Traditional researches of drones mainly focus on their mechanical structures and movement control.The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms.Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission.Implementing different functions on mobile devices can result in different behaviors of drones indirectly.With the development of deep learning in computer vision field,commercial drones equipped with camera can be much more intelligent and even realize autonomous flight.In the past,running deep learning based algorithms on mobile devices is highly computational intensive and time consuming.This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.
基金Project supported by Shenyang Aviation Engine Institute of Aviation Industrial Group(No.2483-9704).
文摘The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functions for the vibration design of a pipeline or pipe system are introduced,namely,the frequency,amplitude,transfer ratio,curvature and deformation energy as options for the optimization process.The genetic algorithms(GA)are adopted as the opti- mization method,in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial.The optimization procedure for all the above ob- jective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN. The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example,and their characteristics are analyzed.Finally we put forward two new objective functions,curvature and deformation energy for pipe system optimization.The calculations show that using the curvature as the objective function can reflect the case of minimal stress,and the optimization results using the deformation energy represent lesser and more uni- form stress distribution.The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement.
文摘BACKGROUND System based practice(SBP) milestones require trainees to effectively navigate the larger health care system for optimal patient care. In gastroenterology training programs, the assessment of SBP is difficult due to high volume, high acuity inpatient care, as well as inconsistent direct supervision. Nevertheless,structured assessment is required for training programs. We hypothesized that objective structured clinical examination(OSCE) would be an effective tool for assessment of SBP.AIM To develop a novel method for SBP milestone assessment of gastroenterology fellows using the OSCE.METHODS For this observational study, we created 4 OSCE stations: Counseling an impaired colleague, handoff after overnight call, a feeding tube placement discussion, and giving feedback to a medical student on a progress note. Twentysix first year fellows from 7 programs participated. All fellows encountered identical case presentations. Checklists were completed by trained standardized patients who interacted with each fellow participant. A report with individual and composite scores was generated and forwarded to program directors to utilize in formative assessment. Fellows also received immediate feedback from a faculty observer and completed a post-session program evaluation survey.RESULTS Survey response rate was 100%. The average composite score across SBP milestones for all cases were 6.22(SBP1), 4.34(SBP2), 3.35(SBP3), and 6.42(SBP4)out of 9. The lowest composite score was in SBP 3, which asks fellows to advocate for cost effective care. This highest score was in patient care 2, which asks fellows to develop comprehensive management plans. Discrepancies were identified between the fellows’ perceived performance in their self-assessments and Standardized Patient checklist evaluations for each case. Eighty-seven percent of fellows agreed that OSCEs are an important component of their clinical training,and 83% stated that the cases were similar to actual clinical encounters. All participating fellows stated that the immediate feedback was "very useful." One hundred percent of the fellows stated they would incorporate OSCE learning into their clinical practice.CONCLUSION OSCEs may be used for standardized evaluation of SBP milestones. Trainees scored lower on SBP milestones than other more concrete milestones. Training programs should consider OSCEs for assessment of SBP.
