检索结果-南通市图书馆

defect detection Model Using Time Series Data Augmentation and Transformation

Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： defect detection time series deep learning data augmentation data transformation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

引用

Biomimetic Intelligence & Robotics 2024年第2期4卷 24-31页

作者： Zhiyu Wan Hai Lan Sichao Lin Houde Dai School of Advanced Manufacturing Fuzhou UniversityJinjiang 362251China Quanzhou Institute of Equipment Manufacturing Haixi InstitutesChinese Academy of SciencesJinjiang 362216China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fujian Special Equipment Inspection and Research InstituteFuzhou 350008China

Customized 3D-printed structural parts are widely used in surgical *** satisfy the mechanical properties and kinematic functions of these structural parts,a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and ***,topology optimization currently faces some practical challenges that must be addressed,such as ensuring that structures do not have significant defects when converted to additive manufacturing *** address this problem,we designed a 3D hierarchical fully convolutional network(FCN)to predict the precise position of the defective *** on the prediction results,an effective repair strategy is adopted to repair the defective structure.A series of experiments is conducted to demonstrate the effectiveness of our *** to the 2D fully convolutional network and the rule-based detection method,our approach can accurately capture most defect structures and achieve 89.88%precision and 95.59%***,we investigate the impact of different ways to increase the receptive field of our model,as well as the trade-off between different defect-repairing *** results of the experiment demonstrate that the hierarchical structure,which increases the receptive field,can substantially improve the defect detection *** the best of our knowledge,this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms,providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

关键词： Topology optimization Additive manufacturing Deep learning 3D semantic segmentation defect detection

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Composite Transformer-Based Multi-Stage defect detection Architecture for Sewer Pipes

引用

Computers, Materials & Continua 2024年第1期78卷 435-451页

作者： Zifeng Yu Xianfeng Li Lianpeng Sun Jinjun Zhu Jianxin Lin School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Environmental Science and Engineering Sun Yat-sen UniversityGuangzhou510275China Guangdong AIKE Environmental Science and Technology Co. Ltd.Zhongshan528400China

Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential *** recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential ***,these models are insufficient in terms of dataset complexity,model versatility and *** work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin *** based on this architecture is trained using a more comprehensive dataset containingmore classes of *** ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different *** model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of *** represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO *** addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.

关键词： Sewer pipe defect detection deep learning model optimization composite transformer

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

引用

Global Energy Interconnection 2024年第3期7卷 284-292页

作者： Chao Huang Minghui Chen Long Wang Department of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083P.R.China Shunde Innovation School University of Science and Technology BeijingFoshan 528399P.R.China

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable *** this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample *** a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional *** features from the generator are passed to the defect detection *** several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection *** small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective *** results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled *** accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

关键词： defect detection Generative adversarial network scSE attention Semi-supervision Wind turbine

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

YOLO-DD:Improved YOLOv5 for defect detection

引用

Computers, Materials & Continua 2024年第1期78卷 759-780页

作者： Jinhai Wang Wei Wang Zongyin Zhang Xuemin Lin Jingxian Zhao Mingyou Chen Lufeng Luo School of Electronic and Information Engineering Foshan UniversityFoshan528000China School of Mechatronic Engineering and Automation Foshan UniversityFoshan528000China

As computer technology continues to advance,factories have increasingly higher demands for detecting ***,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and *** address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and *** improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different *** classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s *** results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization *** high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

关键词： YOLO-DD defect detection feature fusion attention mechanism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Multi-Scale Feature Fusion Model for Bridge Appearance defect detection

引用

Big Data Mining and Analytics 2024年第1期7卷 1-11页

作者： Rong Pang Yan Yang Aiguo Huang Yan Liu Peng Zhang Guangwu Tang School of Computing and Artificial Intelligence Southwest Jiaotong UniversityChengdu 611756China China Merchants Chongqing Road Engineering Inspection Center Co. Ltd.Chongqing 400067China State Key Laboratory of Bridge Engineering Structural Dynamics Chongqing 400067China

Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregular shapes,and strong noise interference in bridge defect *** deal with these issues,this paper proposes a novel Multi-scale Feature Fusion(MFF)model for bridge appearance disease ***,the Faster R-CNN model adopts Region Of Interest(ROl)pooling,which omits the edge information of the target area,resulting in some missed detections and inaccuracies in both detecting and localizing bridge ***,this paper proposes an MFF based on regional feature Aggregation(MFF-A),which reduces the missed detection rate of bridge defect detection and improves the positioning accuracy of the target ***,the Faster R-CNN model is insensitive to small targets,irregular shapes,and strong noises in bridge defect detection,which results in a long training time and low recognition ***,a novel Lightweight MFF(namely MFF-L)model for bridge appearance defect detection using a lightweight network EfficientNetV2 and a feature pyramid network is proposed,which fuses multi-scale features to shorten the training speed and improve recognition ***,the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset.

关键词： defect detection Multi-scale Feature Fusion(MFF) Region Of Interest(ROl)alignment lightweight network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

SDH-FCOS:An Efficient Neural Network for defect detection in Urban Underground Pipelines

引用

Computers, Materials & Continua 2024年第1期78卷 633-652页

作者： Bin Zhou Bo Li Wenfei Lan Congwen Tian Wei Yao College of Computer Science South-Central Minzu UniversityWuhan430074China

Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of *** to the growing demand for defect detection in urban underground pipelines,this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector(FCOS),called spatial pyramid pooling-fast(SPPF)feature fusion and dual detection heads based on FCOS(SDH-FCOS)*** study improved the feature fusion component of the model network based on FCOS,introduced an SPPF network structure behind the last output feature layer of the backbone network,fused the local and global features,added a top-down path to accelerate the circulation of shallowinformation,and enriched the semantic information acquired by shallow *** ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection *** experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately;the average accuracy was improved by 2.7% compared with the original FCOS model,reducing the leakage rate to a large extent and achieving real-time ***,our model achieved a good trade-off between accuracy and speed compared with other mainstream *** proved the effectiveness of the proposed model.

关键词： Urban underground pipelines defect detection SDH-FCOS feature fusion SPPF dual detection heads

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Efficient YOLOX-Based Method for Photovoltaic Cell defect detection

引用

Instrumentation 2024年第2期11卷 83-94页

作者： Junjie Wang Li Bi Xunde Ma Pengxiang Sun College of Information Engineering Ningxia UniversityYinchuan 750021China Hefei Institutes of Physical Science Chinese Academy of SciencesHefei 230031China University of Science and Technology of China Hefei 230026China College of Economics and Management Taiyuan University of TechnologyTaiyuan 030024China

defect detection technology is crucial for the efficient operation and maintenance of photovoltaic ***,the diversity of defect types,scale inconsistencies,and background interference significantly complicate the detection ***,this paper employs the YOLOX model as the backbone network structure and optimizes various modules to address these ***,we adopt a transfer learning strategy to accelerate model convergence and avoid insufficient accuracy due to the limited number of defect ***,we introduce the SENet module into the feature extraction process to enhance the contrast between defects and their ***,we incorporate the ASFF strategy at the end of the PAFPN network to adaptively learn and emphasize both high-and low-level semantic features of ***,model accuracy is enhanced by refining the loss functions for positioning,classification,and ***,the proposed method achieved excellent results on the Photovoltaic Electroluminescence Anomaly detection dataset(PVEL-AD),with a mAP of 96.7%and a detection speed of ***,the detection of small target defects showed significant improvement.

关键词： photovoltaic cell defect detection deep learning YOLOX electroluminescence

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Strip steel surface defect detection algorithm based on improved Faster R-CNN

引用

China Welding 2024年第2期33卷 11-22页

作者：齐继阳吴宇帆 School of Mechanical Engineering Jiangsu University of Science and TechnologyZhenjiang212100China

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is ***,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface *** improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale *** RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect ***,Soft NMS is used to achieve non-maximum suppression and remove redundant *** the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN *** experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

关键词： defect detection RC-Swin Transformer CBAM-BiFPN RoI align Soft NMS

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

defect detection of gear parts in virtual manufacturing

引用

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 76-87页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

关键词： defect detection Gear surface Gear dataset Combinational Convolution Block

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：