IEEE Transactions on Instrumentation and Measurement最新文献

DMPDD-Net: An Effective Defect Detection Method for Aluminum Profiles Surface Defect DMPDD-Net：一种有效的铝型材表面缺陷检测方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497168

Tingting Sui;Junwen Wang

{"title":"DMPDD-Net: An Effective Defect Detection Method for Aluminum Profiles Surface Defect","authors":"Tingting Sui;Junwen Wang","doi":"10.1109/TIM.2024.3497168","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497168","url":null,"abstract":"Defect detection is an important part of the manufacturing process of industrial products. The processing of aluminum profiles requires more accurate and robust defect detection methods. However, various types of defects, small size of the defect pixel area and high defect-background similarity issues pose challenges to existing industrial defect detection methods for aluminum profiles surface defect (APSD). To address these issues, with dual-path parallel attention mechanism (DP-AM), multifeature fusion mechanism (MFFM) and parallel spatial pyramid pooling fast (PSPPF) module, a novel defect detection network, named DMPDD-Net, is proposed in this article. Specifically, the proposed detection network enhances the feature extraction ability of small-size defects by designing a parallel DP-AM module. Meanwhile, a self-learning factor is set up in the feature fusion formula to construct the MFFM module, which enhances the expression ability of defect features for APSD through multichannel feature fuse mechanism. Additionally, the PSPPF module is proposed to perform spatial pyramid pooling (SPP) in You-Only-Look-Once-Version-Eight model (YOLOv8) to reduce the loss of key features. We validate the effectiveness and improvement of our proposed DMPDD-Net by conducting ablation experiments and algorithm comparisons on the Tianchi aluminum profile surface defect dataset (TAPSDD). Our proposed model outperforms the baseline network with a 3.5% increase in mean average precision (mAP)@0.5 and a 2.8% increase in mAP@0.5:0.95, and a 3.2% increase in formula-one-score (F1) for the TAPSDD dataset. Our research results indicate that our proposed network is a promising alternative to the current defect detection methods for APSD.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3-D Millimeter-Wave Imaging for Sparse MIMO Array With Range Migration and l₂-Norm-Reinforced Sparse Bayesian Learning 利用范围迁移和 l₂-规范强化稀疏贝叶斯学习实现稀疏 MIMO 阵列的三维毫米波成像

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497189

Hao Tu;Libin Yu;Zhaolong Wang;Wen Huang;Lei Sang

{"title":"3-D Millimeter-Wave Imaging for Sparse MIMO Array With Range Migration and l₂-Norm-Reinforced Sparse Bayesian Learning","authors":"Hao Tu;Libin Yu;Zhaolong Wang;Wen Huang;Lei Sang","doi":"10.1109/TIM.2024.3497189","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497189","url":null,"abstract":"Sparse multiple-input-multiple-output (MIMO) millimeter-wave (MMW) near-field imaging systems, based on the principle of phase coherence, can reduce the hardware cost and system complexity and improve the speed of perception while ensuring high resolution. Conventional frequency-domain imaging algorithms such as range migration cannot be directly applied to such systems due to the spatial downsampling of the antenna array, while conventional time-domain imaging methods such as back projection are highly computationally ineffective. To address this issue, we propose a two-stage imaging algorithm. The first stage deals with the sparse array as a virtual full array for fast frequency-domain imaging using phase center approximation (PCA). However, the PCA process cannot accurately compensate for the phase errors, especially in near-field imaging scenarios with large field-of-view and undersampling. Thus, in the second step, we introduce a compressive sensing (CS) algorithm based on sparse Bayesian learning (SBL) to correct the phase errors, where an \u0000<inline-formula> <tex-math>$l_{2}$ </tex-math></inline-formula>\u0000 norm term is introduced to balance the sparsity and fidelity of the reconstructed image. The optimization problem is iteratively solved to refocus the imaging results obtained in the first step, leading to 3-D images with high quality. Simulations and experiments confirm that our proposed algorithm achieves high imaging performance with good computational efficiency for a large undersampling ratio (USR).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hydrothermal Synthesis of Ag-Modified CuO/In2O3 Nanospheres for Rapidly Detecting Ppb-Level 2-Butanone 水热法合成用于快速检测ppb级2-丁酮的银修饰CuO/In2O3纳米球

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497156

Zhiqiang Yang;Zhenyu Yuan;Renze Zhang;Jingfeng Li;Hongmin Zhu;Hongliang Gao;Fanli Meng

{"title":"Hydrothermal Synthesis of Ag-Modified CuO/In2O3 Nanospheres for Rapidly Detecting Ppb-Level 2-Butanone","authors":"Zhiqiang Yang;Zhenyu Yuan;Renze Zhang;Jingfeng Li;Hongmin Zhu;Hongliang Gao;Fanli Meng","doi":"10.1109/TIM.2024.3497156","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497156","url":null,"abstract":"2-butanone has been identified as a volatile biomarker for a variety of diseases, and its rapid detection at ppb level is necessary for disease diagnosis. In2O3 is an ideal sensing material for the detection of trace volatile organic compounds (VOCs), achieving high sensitivity and fast detection of 2-butanone when heterojunctions and noble metals are present. In this work, spherical Ag/CuO/In2O3 composites are synthesized via mild hydrothermal method for the efficient detection of 2-butanone by modifying the amount of Ag (3%, 5%, 7%, 9%, and 15%). Characterization results show that CuO and Ag are evenly dispersed across In2O3 surfaces. The test results demonstrate that the 7% Ag-CuO/In2O3 sensor possesses the finest performance toward 2-butanone, achieving response value up to 151.5 (100 ppm) at \u0000<inline-formula> <tex-math>$250~^{circ }$ </tex-math></inline-formula>\u0000C, which is far superior to several other sensors. In addition, it delivers 10-s response time, detects 50-ppb 2-butanone, and presents excellent repeatability and long-term stability. Finally, further mechanism analysis shows that the catalytic activity of CuO and active sites at the heterojunction interface enhance the selectivity for 2-butanone. Next, Ag produces spillover effect to accelerate the gas reaction, and the Ag2O-In2O3 interface and Ag-In2O3 interface interconvert to change the direction of electron movement and energy band structure. In addition, XPS shows that the Ag/CuO/In2O3 composites contain extensive oxygen vacancies and adsorbed oxygen species, which affect the electron depletion layer (EDL). Overall effect from the above factors dramatically improves the 2-butanone sensing performance of Ag/CuO/In2O3 composites. This work sheds fresh insight into the design of high-sensitivity 2-butanone gas sensors for rapid detection of ppb level.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lightweight Reprogramming Framework for Cross-Device Fault Diagnosis in Edge Computing 用于边缘计算中跨设备故障诊断的轻量级重编程框架

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497154

Yanzhi Wang;Jinhong Wu;Shuyang Luo;Ziyang Yu;Qi Zhou

{"title":"A Lightweight Reprogramming Framework for Cross-Device Fault Diagnosis in Edge Computing","authors":"Yanzhi Wang;Jinhong Wu;Shuyang Luo;Ziyang Yu;Qi Zhou","doi":"10.1109/TIM.2024.3497154","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497154","url":null,"abstract":"Fault diagnosis in mechanical equipment is essential for industrial system stability and safety. However, when applying the model to different models of devices, the difference in sample feature distribution seriously affects the diagnosis effect. At the same time, traditional cloud-based deployment faces delays and resource constraints, making it unable to meet real-time requirements. This article introduces a lightweight reprogramming framework for cross-device fault diagnosis in edge computing environments. It mainly includes cloud-based C-model training and edge-based E-model reprogramming and application stages. The model introduces a lightweight feature extraction (LFE) module and a decoupled fully connected (DFC) attention mechanism to enhance feature representation and global information capture. Through lightweight reprogramming, the E-model fits the device data in actual engineering while maintaining the diagnostic capability of the C-model. We used the NVIDIA Jetson Xavier NX kit as an edge computing platform and conducted verification experiments. The results show that the proposed method achieves good diagnostic effects on engineering equipment. At the same time, it achieves excellent lightweight indicators.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

C-DHV: A Cascaded Deep Hough Voting-Based Tracking Algorithm for LiDAR Point Clouds C-DHV：基于级联深度 Hough 投票的激光雷达点云跟踪算法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497183

Anqi Xu;Jiahao Nie;Zhiwei He;Xudong Lv

{"title":"C-DHV: A Cascaded Deep Hough Voting-Based Tracking Algorithm for LiDAR Point Clouds","authors":"Anqi Xu;Jiahao Nie;Zhiwei He;Xudong Lv","doi":"10.1109/TIM.2024.3497183","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497183","url":null,"abstract":"A LiDAR-based 3-D object tracking system has been widely used in various scenarios such as autonomous driving and video surveillance, as it provides real-time and accurate object locations. Existing 3-D object tracking algorithms have achieved success by employing deep Hough voting to generate 3-D proposals. However, only one-stage voting adopted to generate 3-D proposals leads to inaccurate localization and degraded performance in complex scenarios with substantial background distractors and drastic appearance change. In this article, we propose a novel cascaded deep Hough voting (C-DHV) algorithm, which employs multistage voting to iteratively refine the 3-D proposals. Specifically, in each voting stage, the geometric locations and features of 3-D proposals are refined, which provides better initialization for the next voting stage. To improve the discriminative ability of C-DHV, the hierarchical features are fully leveraged by a feature transfer module to guide each voting stage, which enables to fuse the deep-layer features into low-level voting stage. Besides, a transformer-based feature clustering module is developed to adaptively aggregate features of 3-D proposals delivered from multistage voting, which promotes the prediction of the most accurate proposal as the final tracking result. Extensive experiments on challenging KITTI, NuScenes, and Waymo Open Dataset show that our C-DHV achieves competitive performance compared to state-of-the-art methods and significantly outperforms the one-stage voting counterpart.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TTSDA-YOLO: A Two Training Stage Domain Adaptation Framework for Object Detection in Adverse Weather TTSDA-YOLO：用于恶劣天气下物体检测的双训练阶段领域适应框架

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497132

Mengmeng Zhang;Qiyu Rong;Hongyuan Jing

{"title":"TTSDA-YOLO: A Two Training Stage Domain Adaptation Framework for Object Detection in Adverse Weather","authors":"Mengmeng Zhang;Qiyu Rong;Hongyuan Jing","doi":"10.1109/TIM.2024.3497132","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497132","url":null,"abstract":"Object detection plays a crucial role in the fields of autonomous driving, security surveillance, unmanned aerial vehicle (UAV), and so on. However, the performance of detectors can be drastically degraded by adverse weather conditions, such as fog, rain, and snow. This is because detectors are usually trained on images taken in clear weather conditions but tested under adverse weather conditions. There is a domain shift problem between images captured in adverse weather and those taken in clear weather. In this article, we propose a robust detection framework called two training stage domain adaptation you only look once (TTSDA-YOLO), which performs well in both normal and adverse weather conditions based on YOLOv7. We design a new training strategy that fully utilizes auxiliary domains to transfer knowledge from the source domain to the target domain. This training strategy consists of two stages. In the first training stage, we address the disparity in feature distributions between normal weather images and adverse weather images. We use a multiscale image-level domain adaptation (IDA) module to gradually adapt the normal weather domain to the adverse weather domain. In the second training stage, we make full use of the auxiliary domain by inputting it into the network as a training set. To prevent new domain shifts from being generated during the training process, we design a backbone regularization module (BRM). Extensive experimental results of the proposed TTSDA-YOLO on benchmark datasets show that our approach can significantly improve the detection performance of the network in adverse weather conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soft-Magnet-Based Position Estimation Using an Approximate Extended Kalman Filter With a Hybrid Analytical-Neural Network Model 利用分析-神经网络混合模型的近似扩展卡尔曼滤波器进行基于软磁体的位置估计

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497169

Tao Wen;Suqi Liu;Heng Wang

{"title":"Soft-Magnet-Based Position Estimation Using an Approximate Extended Kalman Filter With a Hybrid Analytical-Neural Network Model","authors":"Tao Wen;Suqi Liu;Heng Wang","doi":"10.1109/TIM.2024.3497169","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497169","url":null,"abstract":"Soft-magnet (SM)-based position tracking is a new wireless magnetic tracking method that can reject ferromagnetic disturbances. Conventionally, position estimation is implemented either by a standard extended Kalman filter (EKF) using a dipole-based analytical measurement model or by an EKF or particle filter (PF) using a neural network model. The former method, however, fails to achieve satisfactory estimation accuracy due to the large modeling error, while the latter is time-consuming and fails to achieve real-time tracking. In this article, a hybrid analytical-neural network measurement model is built to improve the modeling accuracy and generalization, which uses a neural network to compensate for the analytical modeling error. An approximate EKF (AEKF) framework is, furthermore, developed to use the hybrid model and improve position estimation accuracy and computational efficiency simultaneously. In the AEKF, only the analytical part is linearized to compute the measurement Jacobian efficiently while the compensated hybrid model is used to compute the innovation (measurement residual) to ensure accuracy. Experimental results show that the root-mean-square (rms) position error ranges from 2.64 to 5.62 mm across the workspace, which rivals the standard EKF and the PF with an accurate pure neural network model. The average update time of the proposed algorithm is, however, only 13.82 ms (update rate: 73 Hz), which is three times faster than the standard EKF using a pure neural network model. In conclusion, the proposed AEKF algorithm with a hybrid model can achieve accurate and real-time position estimation simultaneously with good generalization for the SM-based tracking system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First Arrival Picking of Aircraft-Excited Seismic Waves Based on Energy Distribution 基于能量分布的飞机激发地震波初至选择

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497162

Shuang Li;Tao Jiang;Zhongqin Pang

{"title":"First Arrival Picking of Aircraft-Excited Seismic Waves Based on Energy Distribution","authors":"Shuang Li;Tao Jiang;Zhongqin Pang","doi":"10.1109/TIM.2024.3497162","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497162","url":null,"abstract":"When the acoustic-to-seismic coupling effect is used to detect low-altitude aircrafts, picking up the exact first arrival of the seismic waves excited by aircrafts directly affects the accuracy of the detection. There are random noise and frequent impulse noise in seismic data collection due to wind blowing, grass hitting, and so on. The existing seismic wave picking algorithms are sensitive to the impulse noise, which leads to high false picking rate. In order to solve this problem, first arrival picking of aircraft-excited seismic waves based on energy distribution (ED) is proposed in this article. Compared with the commonly used pickup algorithms, ED can avoid the false pickup caused by single impulse noise and effectively reduce the false picking caused by impulse noise clusters consisted of multiple consecutive single impulse noise. ED ensures both high accuracy and high computational efficiency. More importantly, the actual collected data may contain multiple aircraft events, and ED can pick up all the aircraft events at the same time and does not need to intercept each aircraft signal one by one. By picking up the actual data, the effectiveness and practicability of ED are further verified. The advantages and limitations of the ED algorithm are also discussed to provide future research directions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Process Monitoring and Root Cause Diagnosis Strategy Based on Knowledge-Data-Integrated Causal Digraph for Complex Industrial Processes 基于知识-数据-集成因果关系图的复杂工业过程监控和根本原因诊断新策略

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497056

Jie Dong;Daye Li;Yanmei Wei;Kaixiang Peng

{"title":"A Novel Process Monitoring and Root Cause Diagnosis Strategy Based on Knowledge-Data-Integrated Causal Digraph for Complex Industrial Processes","authors":"Jie Dong;Daye Li;Yanmei Wei;Kaixiang Peng","doi":"10.1109/TIM.2024.3497056","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497056","url":null,"abstract":"With the integrated and scaled development of modern industrial processes, multiple control units are strongly coupled, forming a complex interconnected network. This leads to the propagation and evolution of faults within the network, which will affect the quality of products and the safety of industrial processes. This article proposes a novel process monitoring and root cause diagnosis strategy for complex industrial processes based on a knowledge-data-integrated causal digraph. Compared with traditional single data-driven methods, this strategy combines process data and knowledge to improve the ability of fault detection and diagnosis. First, an attention-based time convolutional network is performed on process variables to construct a causal digraph. The causal digraph is trimmed and refined using the process knowledge to solve the problem of redundant causality and enhance interpretability. Second, the complex industrial process is decomposed into multiple sub-blocks, and the causal relationship between sub-blocks is obtained. On this basis, a process monitoring model for collaborative analysis of temporal and spatial information is established, where spatial information among sub-blocks is obtained through the interaction of information between them, and the temporal information within sub-blocks is captured by kernel canonical variate analysis (KCVA). Subsequently, a fault diagnosis method based on global and local causal digraphs is designed. Process data and causal digraphs are used to select fault variables and analyze causality relationships respectively, which can infer the fault root cause and propagation path. Finally, the experimental results on the real dataset of the float glass production process demonstrate that our strategy not only achieves significant improvements over other methods but also has a favorable application in industrial processes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Modal Collaborative Evolution Reinforced by Semantic Coupling for Image Registration and Fusion 通过语义耦合强化跨模态协作进化，实现图像注册与融合

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-11-13 DOI: 10.1109/TIM.2024.3497157

Yan Xiong;Jun Kong;Yunde Zhang;Ming Lu;Min Jiang

{"title":"Cross-Modal Collaborative Evolution Reinforced by Semantic Coupling for Image Registration and Fusion","authors":"Yan Xiong;Jun Kong;Yunde Zhang;Ming Lu;Min Jiang","doi":"10.1109/TIM.2024.3497157","DOIUrl":"https://doi.org/10.1109/TIM.2024.3497157","url":null,"abstract":"Joint image registration and fusion aim to align and integrate source images to generate an image with salient targets and rich texture details. Current methods pursue spatially optimal deformation fields. However, these methods often overlook local semantic alignment, leading to exacerbated heterogeneity in cascaded fusion and vision tasks. To address these issues, we propose a collaborative evolution network reinforced by semantic coupling for image registration and fusion, named CE-SCNet. First, to correct spatial misalignments, we design a multiscale deformation estimator (MSDE). This module is to estimate spatial deformation fields by modeling global relationships across multiple scales. Second, to further enhance semantic alignment and mitigate heterogeneity, we design the semantic interaction module (SIM). This module is to integrate contextual information within the semantic domain for feature coupling. Third, to reconstruct images with high visual perception, we design the feature discrimination module (FDM) and the detail awareness module (DAM). Both modules are to capture texture information from multiple perspectives. Finally, to optimize the joint paradigm, we construct a multilabel semantic loss. Extensive experimental validations have shown that CE-SCNet significantly outperforms state-of-the-art methods in alleviating semantic misalignments. The semantic segmentation experiments demonstrate that CE-SCNet can adapt to the semantic demands of high-level vision tasks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0