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detection and localization of debonding beneath concrete pavement using transmissibility function analysis

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 159卷 107802-107802页

作者： Wu, Difei Zeng, Mengyuan Zhao, Hongduo Wang, Yang Du, Yuchuan Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai Peoples R China Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This paper proposes a novel method to detect and localize debonding beneath the concrete pavement using transmissibility function (TF) analysis. The layout of the measurement node array, the signal processing approach, and the testing procedures are proposed. Two matrices of transmissibility damage indicator (TDI) are proposed to describe the distribution of debonding. This method is a response-only approach and can offer advantages in easy and low-cost implementation. The theoretical deduction, finite element analysis (FEA), and field tests are conducted to demonstrate and verify detection and localization performance. It is first observed that the arrangement of measurement nodes and the frequency range are crucial to TF analysis. TFs at higher frequencies range are more sensitive to the debonding but may result in higher errors. The density of the measurement node array and the noise's effect are investigated in the FEA. Using a denser node array and maintaining a lower noise level helps localize the debonding more precisely. Field tests are conducted on a precast concrete pavement plate using a specially designed accelerometer array. The test results demonstrate the feasibility of the TF-based method for debonding detection and localization. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Concrete pavement Debonding Transmissibility function Transmissibility damage indicator detection and localization

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Attention mechanism combined with residual recurrent neural network for sound event detection and localization

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2022年第1期2022卷 29-29页

作者： Lan, Chaofeng Zhang, Lei Zhang, Yuanyuan Fu, Lirong Sun, Chao Han, Yulan Zhang, Meng Harbin Univ Sci & Technol Dept Sch Measurement & Commun Engn Harbin 150080 Peoples R China Beidahuang Ind Grp Gen Hosp Harbin 150088 Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Peoples R China Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China

In the task of sound event detection and localization (SEDL) in a complex environment, the acoustic signals of different events usually have nonlinear superposition, so the detection and localization effect is not good. Given this, this paper is based on the Residual-spatially and channel Squeeze-Excitation (Res-scSE) model. Combined with Multiple-scale Convolutional Recurrent Neural Network (M-CRNN), the Res-scSE-CRNN model is proposed. Firstly, to solve the problem of insufficient extraction of time-frequency feature in single-size convolution kernel, multi-scale feature fusion is carried out by using the feature hierarchy of the convolutional neural network to improve the accuracy of detection. Secondly, aiming at the problem of overlapping audio event localization accuracy is not high, with Res-scSE to replace common convolution module and add residual structure to strengthen the feature extraction, and combining with an attention mechanism to enhance neural network channels and spatial relationships, to improve the network to extract the characteristics of directivity, achieve the goal of the overlapped audio localization. In this paper, experiments are carried out in the open dataset DCASE2019, and evaluation indicators are used to analyze the effectiveness of the proposed model and baseline model in the detection and localization of audio events. The results show that compared with the M-CRNN model, the detection error rate of Res-scSE-CRNN model is reduced 4%, the F1-Score is increased 3.4%, the localization error is reduced by 22.8 & DEG;, and the frame recall rate is increased 3%.

关键词： Sound event detection and localization Convolutional cyclic neural network Multi-scale feature fusion Space channel squeeze excitation module

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The equipment detection and localization of large-scale construction jobsite by far-field construction surveillance video based on improving YOLOv3 and grey wolf optimizer improving extreme learning machine

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CONSTRUCTION AND BUILDING MATERIALS 2021年 291卷 123268-123268页

作者： Zeng, Tuocheng Wang, Jiajun Cui, Bo Wang, Xiaoling Wang, Dong Zhang, Yunuo Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

The equipment detection and localization on large-scale construction jobsite by far-field construction surveillance video is significant to monitor jobsite safety and construction process. These videos usually with a larger view containing extensive construction equipment and richer environment information could help the supervisor know more about real-time status of the large-scale construction jobsite. However, due to numerous multi-scale objects, especially for small scale objects in the video, it is difficult to recognize each equipment intuitively, which is not conducive to ensuring jobsite security and guiding construction process. This study presents a real-time multi-scale equipment detection and localization model including the improving YOLOv3 for detection and the grey wolf optimizer improving extreme learning machine (GWO-ELM) for localization. The effectiveness and efficiency of the proposed model are demonstrated via a real hydraulic-engineering megaproject. The experimental results show that the improving YOLOv3 has improved 4.8% mean average precision (mAP) over the multi-scale objects detection, especially, the significant improvements of 17.2% mAP and 13.3% recall at small scale objects detection while maintaining real-time. The improving YOLOv3 is proved robust under various environmental conditions on construction monitoring. Meanwhile, the mean absolute localization percentage errors of different scale objects are less than 1%, which indicates the accuracy and effectiveness of the model to assist supervisor in inspecting construction safety and process. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Far-field construction surveillance video and image detection and localization Real-time Multi-scale Large-scale construction jobsite Deep learning

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Generalized Dechirp-Keystone Transform for Radar High-Speed Maneuvering Target detection and localization

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REMOTE SENSING 2021年第17期13卷 3367-3367页

作者： Zheng, Jibin Zhu, Kangle Niu, Zhiyong Liu, Hongwei Liu, Qing Huo Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450001 Peoples R China Duke Univ Dept Elect Engn Durham NC 27708 USA

The multivariate range function of the high-speed maneuvering target induces modulations on both the envelop and phase, i.e., the range cell migration (RCM) and Doppler frequency migration (DFM) which degrade the long-time coherent integration used for detection and localization. To solve this problem, many long-time coherent integration methods have been proposed. Based on mechanisms of typical methods, this paper names two signal processing modes, i.e., processing unification (PU) mode and processing separation (PS) mode, and presents their general forms. Thereafter, based on the principle of the PS mode, a novel long-time coherent integration method, known as the generalized dechirp-keystone transform (GDKT), is proposed for radar high-speed maneuvering target detection and localization. The computational cost, energy integration, peak-to-sidelobe level (PSL), resolution, and anti-noise performance of the GDKT are analyzed and compared with those of the maximum likelihood estimation (MLE) method and keystone transform-dechirp (KTD) method. With mathematical analyses and numerical simulations, we validate two main superiorities of the GDKT, including (1) the statistically optimal anti-noise performance, and (2) the low computational cost. The real radar data is also used to validate the GDKT. It is worthwhile noting that, based on closed analytical formulae of the MLE method, KTD method, and GDKT, several doubts in radar high-speed maneuvering target detection and localization are mathematically interpreted, such as the blind speed sidelobe (BSSL) and the relationship between the PU and PS modes.

关键词： detection and localization long-time coherent integration range cell migration Doppler frequency migration blind speed sidelobe

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Damage detection and localization in Pipeline Using Sparse Estimation of Ultrasonic Guided Waves Signals

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IFAC-PapersOnLine 2018年第24期51卷 941-948页

作者： Mountassir, Mahjoub El Mourot, Gilles Yaacoubi, Slah Maquin, Didier Institut de soudure Plateforme RDI CND 4 Bvd Henri BecquerelYutz57970 France Centre de Recherche en Automatique de Nancy CNRS Université de Lorraine 2 Avenue de la Forêt de Haye TSA 60604 54 518 France

This paper focuses on the development of a method for damage detection and localization in pipeline structures. These structures are subject to variation of environmental and operational conditions (EOCs) which have an impact on the collected signals. Since damage detection is generally based on comparison between the reference signals and the current signals acquired from the structure, the effects of EOCs will give rise to false alarm. This issue is addressed by selecting from the database of reference signals those with similar or very close EOCs. Such an operation can be performed by calculating a sparse estimation of the current signal. The estimation error is used as an indication of the presence of damage. Actually, a damage signal will be characterized by a high estimation error compared to that of a healthy signal. The position of the detected damage is obtained by calculating the estimation error on a sliding window over the damaged signal. This method was tested on signals collected on a small scale pipeline placed in laboratory conditions. Results have shown that the created damage was successfully detected and localized. © 2018

关键词： Damage detection Errors Guided electromagnetic wave propagation Pipelines Signal processing Structural health monitoring Ultrasonic applications detection and localization environmental Laboratory conditions localization Operational conditions Pipeline structure Sparse estimation Ultrasonic guided wave

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Deep Faster R-CNN-based automated detection and localization of multiple types of damage

Deep Faster R-CNN-based automated detection and localization...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Suh, Gahyun Cha, Young-Jin Univ Manitoba Dept Civil Engn Winnipeg MB Canada

ISBN: (数字)9781510616936

ISBN: (纸本)9781510616936

The primary method of structural health monitoring is human-based visual inspection, which-despite its limitations of consistency and accessibility-can warn about changes in a bridge's condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Region-based Convolutional Neural Network (Faster R-CNN) is proposed(1). The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.

关键词： Computer vision Automated monitoring Non-contact Deep learning Convolutional neural network Region-based network detection and localization

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A partial discharge detection and localization system for high voltage cable based on long-tailed Sagnac interferometric fiber optic sensor

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2017年第9期59卷 2132-2136页

作者： Song, Zhe Guo, Mengjuan Wang, Qi Dalian Jiaotong Univ Sch Sci Dalian 116028 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

A partial discharge detection and localization system for high voltage cable based on a long-tailed optical fiber Sagnac interferometer has been proposed and experimentally demonstrated. In the system, optical fiber is used as the sensing arm and a piezoelectric transducer is used to simulate ultra-acoustic signal of high voltage cable partial discharge. Experimental results show that the accuracy of partial discharge localization is 0.016 km. Compared with traditional partial discharge detecting methods, the detection sensitivity can be improved by using the long-tailed optical fiber Sagnac interferometer.

关键词： detection and localization fiber sagnac interferometer high voltage cable optical fiber sensor partial discharge

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A 3D Simulation Model for Experimental Analysis from Moving Target detection and localization using UWB Radar System 6

A 3D Simulation Model for Experimental Analysis from Moving ...

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6th International Conference on Computational Intelligence and Communication Networks

作者： Nguyen, N. T. Minh Int Univ VNU HCMC Sch Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781479969296

In this paper, we propose a 3D simulation model to characterize the scattered far-field by a scenario. The scenario is composed of a metallic plate representing wall and a trihedral, which is located behind the wall in non-line-of-sight (NLOS) areas. This model is based on a ray-tracing (RT) technique combined with the asymptotic method Uniform Theory of Diffraction (UTD). Two ultra-wideband (UWB) radars are configured as transmitter and receiver, able to work in bistatic or monostatic mode. The modeling is useful not only in explicating the mechanisms of wave propagation through the observed area, but also in evaluating the amplitude and the phase of any given point in the far-zone above the ground. Furthermore, our model is tested against 33-37GHz (Ka-band) indoor measurements conducted in the anechoic chamber. These comparisons have shown that the model gives a good agreement with the experimental results.

关键词： detection and localization moving target simulation model ultra-wideband

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Multi-Scale Proposal Regions Fusion Network for detection and 3D localization of the Infected Trees 3

Multi-Scale Proposal Regions Fusion Network for Detection an...

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3rd International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Hou, Junling Li, Weihong Gong, Weiguo Wang, Zixu Chongqing Univ Key Lab Optoelect Technol & Syst Educ Minist Chongqing Peoples R China

ISBN: (纸本)9781728176383

Forest surveillance towers have the advantages of long observation time, wide observation range, stable and real-time observation. In this paper, a multi-scale proposal regions fusion network (MFRPN) is proposed for detecting the infected trees automatically on the enhanced images from the forest surveillance towers, which can solve the problem that small and large targets can't be effectively detected on a single scale. The proposed MFRPN includes multi-scale images, three CNNs, three different RPNs, and proposal regions fusion model. In the proposed method, we train and run the scale-specific detectors in a multi-task fashion. And, to obtain the accurate spatial level location information of the infected trees, we achieve the three-dimensional (3D) coordinates localization of the digital elevation model (DEM) by using the principle of forest surveillance towers imaging and terrain elevation data. The experimental results show the detection accuracy achieves 91.63%, the detection time of a single image is 0.46 second, and the 3D localization error is less than 50m. The proposed network can realize the real-time detection and 3D localization of the infected trees.

关键词： multi-scale detection and localization the infected trees deep learning

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Real-time social distance monitoring and face mask detection based Social-Scaled-YOLOv4, DeepSORT and DSFD&MobileNetv2 for COVID-19

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 30613-30639页

作者： Mokeddem, Mohammed Lakhdar Belahcene, Mebarka Bourennane, Salah M Khider Univ RB IAIM LI3C Biskra Algeria Ecole Cent GSM Fresnel Inst Marseille France

COVID-19 has spread rapidly worldwide, despite the availability of vaccines, the fear of the World Health Organization continues due to the mutation of the Coronavirus. This is what prompted us to propose this work of social distance and wearing a face mask to fight against this pandemic to save lives. In this work, we propose a real-time four-stage model with monocular camera and deep learning based framework for automating the task of monitoring social distancing and face mask detection using video sequences. This work based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model, Simple Online and Real-time Tracking with a deep association metric approach to tracking people. The perspective transformation is used to approximate the three-dimensional coordinates with Euclidean metric to compute distance between boxes. The Dual Shot Face Detector (DSFD) and MobileNetv2 face mask model used to detect faces of people who violate or cross the social distance. Accuracy of 56.2% and real-time performance of 32 frames per second are achieved by the Social-Scaled-YOLOv4 (Social-YOLOv4-P6) model trained on the MS COCO dataset and Google-Open-Image dataset. The results are compared with other popular state-of-the-art models in terms of Mean-Average-Precision, frame rate and loss of values. The DSFD&MobileNetv2 facemask detectors trained on Wider Face and Real Face mask dataset achieves an accuracy of 99.3%. The proposed approach is validated on indoor/outdoor public images and video sequences such as wider face dataset, Oxford Town Center dataset and open access sequences.

关键词： COVID-19 detection and localization Deep Learning Social Distancing Scaled-YOLOv4 Tracking

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