期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到576篇文章
< 1 2 … 29 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
From microstructure to performance optimization:Innovative applications of computer vision in materials science 认领 引用
1
作者 Chunyu Guo Xiangyu Tang +10 位作者 Yu’e Chen Changyou Gao Qinglin Shan Heyi Wei Xusheng Liu Chuncheng Lu Meixia Fu Enhui Wang Xinhong Liu Xinmei Hou Yanglong Hou 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第1期94-115,共22页
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear... The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects. 展开更多
关键词 microstructure deep learning computer vision performance prediction image generation
暂未订购 下载PDF
Introduction to the Special Issue on Recent Advances in Signal Processing and Computer Vision 认领 引用
2
作者 Bo Yang Chao Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期33-36,共4页
Over the past decade, artificial intelligence, particularly deep learning, has fundamentally reshaped the fields of signal processing and computer vision. As mentioned in the introduction to this special issue, we are... Over the past decade, artificial intelligence, particularly deep learning, has fundamentally reshaped the fields of signal processing and computer vision. As mentioned in the introduction to this special issue, we are witnessing a significant paradigm shift. AI has evolved from recognizing the world through classification and detection to simulating it through generative models and synthesis. Most recently, AI has begun to impact the real world through embodied intelligence and robotic interaction. This special issue reflects this trajectory, presenting original research articles, reviews and methodological advances in areas such as multimodal learning, 3D vision, generative modelling, medical image analysis and autonomous systems.The 12 contributions included here reflect the current frontiers of AI, shedding light on the challenges and opportunities involved in bridging the gap between virtual intelligence and physical reality.Reliable and interpretable AI is of paramount importance in medical applications. This issue contains several papers that address various aspects of this challenge, ranging from signal-enhanced diagnosis to surgical perception and 3D reconstruction. 展开更多
关键词 deep learning robotic interaction signal processing computer vision generative models embodied intelligence recognizing world simulating
暂未订购 下载PDF
Total score of the computer vision syndrome questionnaire predicts refractive errors and binocular vision anomalies 认领 引用
3
作者 Mosaad Alhassan Tasneem Samman +5 位作者 Hatoun Badukhen Muhamad Alrashed Balsam Alabdulkader Essam Almutleb Tahani Alqahtani Ali Almustanyir 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第1期90-96,共7页
AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A... AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users. 展开更多
关键词 computer vision syndrome refractive errors accommodation vergence binocular vision symptoms
暂未订购 下载PDF
Monitoring of Drill-and-Blast Workflows at the Tunnel Face Using Computer Vision and Context Reasoning 认领 引用
4
作者 Chuanjiang Chen Junyong Zhou +3 位作者 Binbin Du Miaosi Dong Liwen Zhang Bitang Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期560-585,共26页
Computer vision has been widely adopted in intelligent construction monitoring;however,existing studies primarily focus on identifying individual construction elements or isolated activities,with limited capability fo... Computer vision has been widely adopted in intelligent construction monitoring;however,existing studies primarily focus on identifying individual construction elements or isolated activities,with limited capability for integrated monitoring of complete construction workflows.Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites.To address the challenge of reliable visual recognition in drill-andblast tunnel environments characterized by uneven illumination,localized glare,and dust interference,this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning.The framework consists of three components:(1)a construction workflow model with a sequence library database,(2)a robust construction element recognition model combining an enhanced YOLOvll with Segment Anything Model 2(SAM2),and(3)a hierarchical workflow reasoning mechanism driven by domain knowledge.A hierarchical workflow model embedding procedural logic is established through field investigation and normative analysis.SAM2 is employed for automated dataset annotation,while YOLOvll is structurally enhanced with Convolutional Block Attention Module(CBAM),Adaptive Feature Enhancement(AFE),and Swin Transformer modules to improve feature representation and adaptability to degraded visual conditions.Workflow identification is finally achieved by integrating visual perception outputs with hierarchical context reasoning.Validation in an active drill-and-blast tunnel shows that the proposed method attains an average detection precision of 91.1%across 1l construction element categories,exceeding 95%for large equipment,and an average workflow recognition accuracy of 94%.The results demonstrate the effectiveness of the proposed framework for monitoring the tunnel construction workflow and supporting construction management. 展开更多
关键词 Drill-and-blast tunnel construction workflow intelligent construction monitoring computer vision workflow recognition context reasoning
暂未订购 下载PDF
Privacy-Preserving Gender-Based Customer Behavior Analytics in Retail Spaces Using Computer Vision 认领 引用
5
作者 Ginanjar Suwasono Adi Samsul Huda +4 位作者 Griffani Megiyanto Rahmatullah Dodit Suprianto Dinda Qurrota Aini Al-Sefy Ivon Sandya Sari Putri Lalu Tri Wijaya Nata Kusuma 《Computers, Materials & Continua》 SCIE EI 2026年第1期1839-1861,共23页
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta... In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy. 展开更多
关键词 Business intelligence customer behavior privacy-preserving analytics computer vision deep learning smart retail gender recognition heatmap privacy RCA-TVGender dataset
暂未订购 下载PDF
A computer vision-based approach for lunar laser ranging signal detection 认领 引用
6
作者 Chengkai Zhou Han Yao +6 位作者 Hui Li Ning Xu Xida Han Xianlin Wu Xudong Lin Ming Li Hongchao Zhao 《Astronomical Techniques and Instruments》 CSCD 2026年第4期370-377,共8页
Lunar laser ranging represents the forefront of laser ranging technology.The considerable distance to the Moon and strong background light mean that the signal-to-noise ratio of lunar laser ranging signals is much low... Lunar laser ranging represents the forefront of laser ranging technology.The considerable distance to the Moon and strong background light mean that the signal-to-noise ratio of lunar laser ranging signals is much lower compared with that of satellite laser ranging signals in Earth’s orbit.Accurate processing of lunar laser ranging data is essential for its scientific applications.This paper addresses the challenge of the low signal-to-noise ratio in lunar laser ranging signals by developing a detection model based on computer vision techniques.The model was tested using both simulated data with a signal-to-noise ratio as low as−19 dB and measured data as low as−12.9 dB,with the results demonstrating that the model provides reliable detection in both cases,offering a robust approach for the effective processing of lunar laser ranging data. 展开更多
关键词 Lunar laser ranging Signal detection Computer vision Random sampling Low signal-to-noise ratio signals
暂未订购 下载PDF
A Survey of Zero-Shot Learning for Fine-Grained Computer Vision 认领 引用
7
作者 Wang Yisong Feng Jianhua +3 位作者 Yang Tianqi Gan Jingfa Ma Rui Jin Yutang 《贵州大学学报（自然科学版）》 2026年第4期1-22,共22页
Zero-Shot Learning(ZSL)in computer vision aims to mitigate the strong reliance of deep learning models on annotated data,thereby advancing the development of machine intelligence.Although early studies have primarily ... Zero-Shot Learning(ZSL)in computer vision aims to mitigate the strong reliance of deep learning models on annotated data,thereby advancing the development of machine intelligence.Although early studies have primarily centered on simple image classification,these tasks are no longer adequate to satisfy the increasing demands for intelligent visual understanding.As a result,ZSL has been expanded to a range of fine-grained computer vision tasks.In this survey,we present a comprehensive review of ZSL methods across four representative fine-grained tasks(i.e.,compositional zero-shot learning,zero-shot semantic segmentation,zero-shot domain generalization,and multi-label zero-shot learning).In examining zero-shot learning,we delve into its formal definition,explore representative approaches,evaluate benchmark datasets,and identify the key challenges that persist.This survey aims to offer a comprehensive overview of the ZSL field,emphasizing the importance of fine-grained classification,and to motivate further advancements in this area. 展开更多
关键词 zero-shot learning compositional zero-shot learning zero-shot semantic segmentation zero-shot domain generalization multi-label zero-shot learning computer vision
暂未订购 下载PDF
Research on the Application of Computer Vision in Equipment Fault Diagnosis 认领 引用
8
作者 Xiaoquan Zhu Siyu Wang +1 位作者 Tong Zhang Pengyuan Chen 《Journal of Electronic Research and Application》 2026年第4期191-198,共8页
With the continuous improvement of industrial automation,rapid and accurate diagnosis of equipment faults is the key to ensuring production safety and efficiency.With the advantages of non-contact sensing,real-time pr... With the continuous improvement of industrial automation,rapid and accurate diagnosis of equipment faults is the key to ensuring production safety and efficiency.With the advantages of non-contact sensing,real-time processing and high-precision recognition,computer vision has broad application prospects in fault diagnosis.This technology integrates image acquisition,feature extraction and deep learning models to automatically identify and classify equipment faults such as appearance damage,motion abnormalities and thermal state changes.Multi-modal image fusion further improves fault positioning accuracy under complex working conditions.In scenarios such as mine electrical equipment,construction engineering inspection cold-chain storage and unmanned aerial vehicle(UAV)inspection,its detection performance is superior to traditional methods,providing strong technical support for building an intelligent equipment operation and maintenance system and promoting the in-depth integration of industrial Internet and intelligent manufacturing. 展开更多
关键词 Computer vision Equipment fault diagnosis Deep learning Feature extraction Image recognition
暂未订购 下载PDF
Computer vision-based real-time tracking and virtual simulation of construction behavior 认领 引用
9
作者 ZHU Li TIAN Ruizhu +3 位作者 GUO Jiachao LI Jiahuan LIU Wei ZHAO Guanyuan 《Journal of Southeast University(English Edition)》 EI CAS 2025年第4期446-456,共11页
To improve the safety of construction workers and help workers remotely control humanoid robots in construc-tion,this study designs and implements a computer vision based virtual construction simulation system.For thi... To improve the safety of construction workers and help workers remotely control humanoid robots in construc-tion,this study designs and implements a computer vision based virtual construction simulation system.For this pur-pose,human skeleton motion data are collected using a Ki-nect depth camera,and the obtained data are optimized via abnormal data elimination,smoothing,and normalization.MediaPipe extracts three-dimensional hand motion coordi-nates for accurate human posture tracking.Blender is used to build a virtual worker and site model,and the virtual worker motion is controlled based on the quaternion inverse kinematics algorithm while limiting the joint angle to en-hance the authenticity of motion simulation.Experimental results show that the system frame rate is stable at 60 frame/s,end-to-end delay is less than 20 ms,and virtual task comple-tion time is close to the real scene,verifying its engineering applicability.The proposed system can drive virtual work-ers to perform tasks and provide technical support for con-struction safety training. 展开更多
关键词 virtual construction computer vision motion control human motion posture tracking simulation
暂未订购 下载PDF
Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision 认领 引用 被引量：6
10
作者 JIA Jingwei NI Youhao +2 位作者 MAO Jianxiao XU Yinfei WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2025年第3期278-285,共8页
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve... To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%. 展开更多
关键词 bridge precast components section geometry parameters size identification computer vision deep learning
暂未订购 下载PDF
A Survey of Adversarial Examples in Computer Vision:Attack,Defense,and Beyond 认领 引用 被引量：3
11
作者 XU Keyizhi LU Yajuan +1 位作者 WANG Zhongyuan LIANG Chao 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第1期1-20,共20页
Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca... Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field. 展开更多
关键词 computer vision adversarial examples adversarial attack adversarial defense
暂未订购 下载PDF
Data-driven assessment of lithium-ion battery degradation using thermal patterns from computer vision 认领 引用
12
作者 Zihan Li Haiyan Tu +4 位作者 Hailong Wang Linyu Hu Shunpeng Chen Ruiting Yan Xin He 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第6期852-859,I0017,共8页
Accurate estimation on the state of health(SOH)is essential for ensuring the safe and reliable operation of batteries.Traditional assessment methods primarily focus on electrical attributes for capacity decay,often ov... Accurate estimation on the state of health(SOH)is essential for ensuring the safe and reliable operation of batteries.Traditional assessment methods primarily focus on electrical attributes for capacity decay,often overlooking the impact of thermal distribution on battery aging.However,thermal effect is a critical factor for degradation process and associated risks throughout their service life.In this paper,we introduce a novel deep learning framework specially designed to estimate the capacity and thermal risks of lithium-ion batteries(LIBs).This model consists of two main components that leverage computer vision technology.One predicts battery capacity by integrating the advantages of thermal and electrical features using a temporal pattern attention(TPA)mechanism,while the other assesses thermal risk by incorporating temperature variation to provide early warnings of potential hazards.An infrared camera is deployed to record temperature evolution of LIBs during the electrochemical process.The thermal heterogeneities are recorded by infrared camera,and the corresponding temperature evolutions are extracted as representative features for analysis.The proposed model demonstrates high accuracy and stability,with an average root mean square error(RMSE)of 0.67% for capacity estimation and accuracy exceeding 93.9% for risk prediction,underscoring the importance of integrating spatial temperature distribution into battery health assessments.This work offers valuable insights for the development of intelligent and robust battery management systems. 展开更多
关键词 Temperature distribution Deep learning Capacity estimation Temporal pattern attention mechanism Computer vision
暂未订购 下载PDF
Enhancing Military Visual Communication in Harsh Environments Using Computer Vision Techniques 认领 引用
13
作者 Shitharth Selvarajan Hariprasath Manoharan +2 位作者 Taher Al-Shehari Nasser A Alsadhan Subhav Singh 《Computers, Materials & Continua》 SCIE EI 2025年第8期3541-3557,共17页
This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the... This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images.Furthermore,the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations.Thus,it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors.The proposed method offers additional robust feature representations by imposing a limiting factor to reduce overall scattering values.This is achieved by visualizing a graphical function.Moreover,to derive valuable insights from a series of photos,both the separation and in-version processes are conducted.This involves analyzing comparison results across four different scenarios.The results of the comparative analysis show that the proposed method effectively reduces the difficulties associated with time and space to 1 s and 3%,respectively.In contrast,the existing strategy exhibits higher complexities of 3 s and 9.1%,respectively. 展开更多
关键词 Image enhancement visual information harsh environment computer vision
暂未订购 下载PDF
Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology 认领 引用 被引量：9
14
作者 Qinjun Qiu Zhong Xie +5 位作者 Die Zhang Kai Ma Liufeng Tao Yongjian Tan Zhipeng Zhang Baode Jiang 《Journal of Earth Science》 SCIE CAS CSCD 2023年第5期1418-1432,共15页
The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensi... The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures. 展开更多
关键词 geological hazard computer vision knowledge graph city safety ontology
暂未订购 下载PDF
A review of the research and application of deep learning-based computer vision in structural damage detection 认领 引用 被引量：19
15
作者 Zhang Lingxin Shen Junkai Zhu Baijie 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2022年第1期1-21,共21页
Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detect... Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid. 展开更多
关键词 deep learning damage detection computer vision loss assessment
暂未订购 下载PDF
Behavioral response of tilapia (Oreochromis niloticus) to acute ammonia stress monitored by computer vision 认领 引用 被引量：8
16
作者 徐建瑜 苗香雯 +1 位作者 刘鹰 崔绍荣 《Journal of Zhejiang University-SCIENCE B》 EI CAS 2005年第8期812-816,共5页
The behavioral responses of a tilapia (Oreochromis niloticus) school to low (0.13 mg/L), moderate (0.79 mg/L) and high (2.65 mg/L) levels of unionized ammonia (UIA) concentration were monitored using a computer vision... The behavioral responses of a tilapia (Oreochromis niloticus) school to low (0.13 mg/L), moderate (0.79 mg/L) and high (2.65 mg/L) levels of unionized ammonia (UIA) concentration were monitored using a computer vision system. The swimming activity and geometrical parameters such as location of the gravity center and distribution of the fish school were calculated continuously. These behavioral parameters of tilapia school responded sensitively to moderate and high UIA concen-tration. Under high UIA concentration the fish activity showed a significant increase (P<0.05), exhibiting an avoidance reaction to high ammonia condition, and then decreased gradually. Under moderate and high UIA concentration the school’s vertical location had significantly large fluctuation (P<0.05) with the school moving up to the water surface then down to the bottom of the aquarium alternately and tending to crowd together. After several hours’ exposure to high UIA level, the school finally stayed at the aquarium bottom. These observations indicate that alterations in fish behavior under acute stress can provide important in-formation useful in predicting the stress. 展开更多
关键词 Ammonia stress Tilapia Computer vision Aquaculture
暂未订购 下载PDF
Surface Characteristics Measurement Using Computer Vision:A Review 认领 引用 被引量：3
17
作者 AbdulWahab Hashmi Harlal Singh Mali +2 位作者 Anoj Meena Mohammad Farukh Hashmi Neeraj Dhanraj Bokde 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期917-1005,共89页
Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered e... Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered equally significant to dimensional quality.Surface Roughness(Ra)is a widely recognized measure to evaluate and investigate the surface quality of machined parts.Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100%inspection and examination because of the time and efforts involved in performing the measurement.However,Machine vision has emerged as the innovative approach to executing the surface roughness measurement.It can provide economic,automated,quick,and reliable solutions.This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics,i.e.,surface roughness,waviness,flatness,surface texture,etc.,machine vision parameters.This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement. 展开更多
关键词 Machine vision surface roughness computer vision machining parameters surface characterization
暂未订购 下载PDF
Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda(Lepidoptera:Noctuidae)based on computer vision 认领 引用 被引量：2
18
作者 LÜChun-yang GE Shi-shuai +4 位作者 HE Wei ZHANG Hao-wen YANG Xian-ming CHU Bo WU Kong-ming 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2023年第7期2173-2187,共15页
Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The ... Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level. 展开更多
关键词 Spodoptera frugiperda computer vision ovary testis WeChat applet
暂未订购 下载PDF
Current applications of artificial intelligence-based computer vision in laparoscopic surgery 认领 引用 被引量：4
19
作者 Kangwei Guo Haisu Tao +4 位作者 Yilin Zhu Baihong Li Chihua Fang Yinling Qian Jian Yang 《Laparoscopic, Endoscopic and Robotic Surgery》 2023年第3期91-96,共6页
Recent advances in artificial intelligence(AI)have sparked a surge in the application of computer vision(CV)in surgical video analysis.Laparoscopic surgery produces a large number of surgical videos,which provides a n... Recent advances in artificial intelligence(AI)have sparked a surge in the application of computer vision(CV)in surgical video analysis.Laparoscopic surgery produces a large number of surgical videos,which provides a new opportunity for improving of CV technology in laparoscopic surgery.AI-based CV techniques may leverage these surgical video data to develop real-time automated decision support tools and surgeon training systems,which shows a new direction in dealing with the shortcomings of laparoscopic surgery.The effectiveness of CV applications in surgical procedures is still under early evaluation,so it is necessary to discuss challenges and obstacles.The review introduced the commonly used deep learning algorithms in CV and described their usage in detail in four application scenes,including phase recognition,anatomy detection,instrument detection and action recognition in laparoscopic surgery.The currently described applications of CV in laparoscopic surgery are limited.Most of the current research focuses on the identification of workflow and anatomical structure,while the identification of instruments and surgical actions is still awaiting further breakthroughs.Future research on the use of CV in laparoscopic surgery should focus on applications in more scenarios,such as surgeon skill assessment and the development of more efficient models. 展开更多
关键词 Artificial intelligence Computer vision Deep learning Laparoscopic surgery
暂未订购 下载PDF
Review of Fabric Defect Detection Based on Computer Vision 认领 引用 被引量：5
20
作者 朱润虎 辛斌杰 +1 位作者 邓娜 范明珠 《Journal of Donghua University(English Edition)》 CAS 2023年第1期18-26,共9页
In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the ov... In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the overall structure of the fabric defect detection system is introduced and some mature detection systems are studied.Then the fabric detection methods are summarized,including structural methods,statistical methods,frequency domain methods,model methods and deep learning methods.In addition,the evaluation criteria of automatic detection algorithms are discussed and the characteristics of various algorithms are analyzed.Finally,the research status of this field is discussed,and the future development trend is predicted. 展开更多
关键词 computer vision fabric defect detection algorithm evaluation textile inspection
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 29 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈