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Damage detection with Image Processing: a comparative study

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Earthquake Engineering and Engineering Vibration 2023年第2期22卷 333-345页

作者： Marianna Crognale Melissa De Iuliis Cecilia Rinaldi Vincenzo Gattulli Department of Structural and Geotechnical Engineering Sapienza University of Romevia Eudossiana 18Rome 00167Italy

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by Image Processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by Image Processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

关键词： damage detection Image Processing image classification civil infrastructure inspection structural health monitoring analysis

Image Processing based three-dimensional model reconstruction for cross-platform numerical simulation

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China Foundry 2023年第2期20卷 139-147页

作者： Yu-cheng Sun Yu-hang Huang Na Li Xiao Han Ai-long Jiang Jin-wu Kang Ji-wu Wang Hai-liang Yu State Key Laboratory of Engine Reliability Weichai Power Co.Ltd.Weifang 261061ShandongChina School of Mechanical Engineering Central South UniversityChangsha 41000China School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Key Laboratory for Advanced Materials processing Technology School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different computing tools have to be developed so as to solve particular fields at different scales and for different processes.Therefore,the integration of different types of software is inevitable.However,it is difficult to perform the transfer of the meshes and simulated results among software packages because of the lack of shared data formats or encrypted data formats.An Image Processing based method for three-dimensional model reconstruction for numerical simulation was proposed,which presents a solution to the integration problem by a series of slice or projection images obtained by the post-processing modules of the numerical simulation software.By means of mapping image pixels to meshes of either finite difference or finite element models,the geometry contour can be extracted to export the stereolithography model.The values of results,represented by color,can be deduced and assigned to the meshes.All the models with data can be directly or indirectly integrated into other software as a continued or new numerical simulation.The three-dimensional reconstruction method has been validated in numerical simulation of castings and case studies were provided in this study.

关键词： cross-platform numerical simulation 3D model reconstruction Image Processing slice

Jointly beam stealing attackers detection and localization without training:an Image Processing viewpoint

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Frontiers of Computer Science 2023年第3期17卷 145-160页

作者： Yaoqi YANG Xianglin WEI Renhui XU Weizheng WANG Laixian PENG Yangang WANG College of Communication Engineer Army Engineering University of PLANanjing 210000China The 63rd Research Institute National University of Defense TechnologyNanjing 210007China Department of Computer Science City University of Hong KongHong Kong 999077China

Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications.The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation.Current consistency-based methods are only valid for one single attacker and are parametersensitive.From the viewpoint of Image Processing,this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI(Received Signal Strength Indicator)map without the training process involved in deep learning-based solutions.Firstly,an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost.Secondly,three Image Processing steps,including edge detection and segmentation,are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers.To evaluate our proposal’s performance,a series of experiments are conducted based on the collected data.Experimental results have shown that in typical parameter settings,our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

关键词： beam-stealing attacks detection localization Image Processing

Detection of abnormal hydroponic lettuce leaves based on Image Processing and machine learning

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Information processing in Agriculture 2023年第1期10卷 1-10页

作者： Ruizhe Yang Zhenchao Wu Wentai Fang Hongliang Zhang Wenqi Wang Longsheng Fu Yaqoob Majeed Rui Li Yongjie Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Xi’an Agriculture Machinery Management and Extension Station Xi’anShaanxi 710065China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China Faculty of Agricultural Engineering and Technology University of AgricultureFaisalabad 38000Pakistan Center for Precision and Automated Agricultural Systems Washington State UniversityProsserWA 99350USA Suide County Lanhuahua Ecological Food Co. Ltd.SuideShaanxi 718000China

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

关键词： Hydroponic lettuce Machine learning Image Processing Abnormal leaves Automatic sorting

A Pixel–Channel Hybrid Attention Model for Image Processing

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Tsinghua Science and Technology 2022年第5期27卷 804-816页

作者： Qiang Hua Liyou Chen Pan Li Shipeng Zhao Yan Li College of Mathematics and Information Science Hebei UniversityBaoding 071002China Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal UniversityZhuhai 519087China

In the field of Image Processing,better results can often be achieved through the deepening of neural network layers involving considerably more parameters.In image classification,improving classification accuracy without introducing too many parameters remains a challenge.As for image conversion,the use of the conversion model of the generative adversarial network often produces semantic artifacts,resulting in images with lower quality.Thus,to address the above problems,a new type of attention module is proposed in this paper for the first time.This proposed approach uses the pixel-channel hybrid attention(PCHA) mechanism,which combines the attention information of the pixel and channel domains.The comparative results of using different attention modules on multiple-image data verify the superiority of the PCHA module in performing classification tasks.For image conversion,we propose a skip structure(S-PCHA model) in the up-and down-sampling processes based on the PCHA model.The proposed model can help the algorithm identify the most distinctive semantic object in a given image,as this structure effectively realizes the intercommunication of encoder and decoder information.Furthermore,the results showed that the attention model could establish a more realistic mapping from the source domain to the target domain in the image conversion algorithm,thus improving the quality of the image generated by the conversion model.

关键词： deep learning attention mechanism image classification Image Processing convolutional neural networks

Dynamic modeling and damage analysis of debris cloud fragments produced by hypervelocity impacts viaImage Processing

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Frontiers of Information Technology & Electronic Engineering 2022年第4期23卷 555-570页

作者： Ru ZENG Yan SONG Weizhen LV Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai200093China

It is always a challenging task to model the trajectory and make an efficient damage estimation of debris clouds produced by hypervelocity impact(HVI)on thin-plates due to the difficulty in obtaining high-quality fragment images from experiments.To improve the damage estimation accuracy of HVIs on a typical double-plate Whipple shield configuration,we investigate the distributive characteristic of debris clouds in successive shadowgraphs using Image Processing techniques and traditional numerical methods.The aim is to extract the target movement parameters of a debris cloud from the acquired shadowgraphs using Image Processing techniques and construct a trajectory model to estimate the damage with desirable performance.In HVI experiments,eight successive frames of fragment shadowgraphs are derived from a hypervelocity sequence laser shadowgraph imager,and four representative frames are selected to facilitate the subsequent feature analysis.Then,using Image Processing techniques,such as denoising and segmentation techniques,special fragment features are extracted from successive images.Based on the extracted information,image matching of debris is conducted and the trajectory of debris clouds is modeled according to the matched debris.A comparison of the results obtained using our method and traditional numerical methods shows that the method of obtaining hypervelocity impact experimental data through Image Processing will provide critical information for improving numerical simulations.Finally,an improved estimation of damage to the rear wall is presented based on the constructed model.The proposed model is validated by comparing the estimated damage to the actual damage to the rear wall.

关键词： Debris clouds Hypervelocity impact Image Processing Damage estimation

Spatial resolution and Image Processing for pinhole camera-based X-ray fluorescence imaging: a simulation study

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Nuclear Science and Techniques 2022年第5期33卷 135-153页

作者： Ze He Ning Huang Peng Wang Zi-Han Chen Bo Peng Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and TechnologySichuan UniversityChengdu 610064China

Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reconstruction.The main objectives were:(1)calculating the quantum efficiency curves of specific cameras,(2)studying the relationships between the spatial resolution and the pinhole diameter,magnification,and camera binning value,and(3)comparing image-processing methods for pinhole camera systems.Several results were obtained using a point and plane source as the X-ray fluorescence emitter and an array of 100×100 silicon pixel detectors as the X-ray camera.The quantum efficiency of a back-illuminated deep depletion(BI-DD)structure was above 30%for the XRF energies in the 0.8–9 keV range,with the maximum of 93.7%at 4 keV.The best spatial resolution of the pinhole camera was 24.7μm and 31.3 lp/mm when measured using the profile function of the point source,with the diameter of 20μm,magnification of 3.16,and camera bin of 1.A blind deconvolution algorithm with Gaussian filtering performed better than the Wiener filter and Richardson iterative methods on FF-XRF images,with the signal-to-noise ratio of 7.81 dB and improved signalto-noise ratio of 7.24 dB at the diameter of 120μm,magnification of 1.0,and camera bin of 1.

关键词： Full-field X-ray fluorescence(FF-XRF) X-ray pinhole camera Spatial resolution Image Processing

An Advanced Image Processing Technique for Backscatter-Electron Data by Scanning Electron Microscopy for Microscale Rock Exploration

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Journal of Earth Science 2024年第1期35卷 301-305页

作者： Zhaoliang Hou Kunfeng Qiu Tong Zhou Yiwei Cai Frontiers Science Center for Deep-time Digital Earth China University of GeosciencesBeijing 100083China Department of Geology University of ViennaVienna 1090Austria State Key Laboratory of Geological Processes and Mineral Resources School of Earth Sciences and ResourcesChinaUniversity of GeosciencesBeijing 100083China

Backscatter electron analysis from scanning electron microscopes(BSE-SEM)produces high-resolution image data of both rock samples and thin-sections,showing detailed structural and geochemical(mineralogical)information.This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using Image Processing techniques.Although Image Processing is a powerful tool for revealing the more subtle data“hidden”in a picture,it is not a commonly employed method in geoscientific microstructural analysis.Here,we briefly introduce the general principles of Image Processing,and further discuss its application in studying rock microstructures using BSE-SEM image data.

关键词： Image Processing rock microstructures electron-based imaging data mining

The early Japanese books reorganization by combining Image Processing and deep learning

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 627-643页

作者： Bing Lyu Hengyi Li Ami Tanaka Lin Meng Graduate School of Science and Engineering Ritsumeikan UniversityKusatsuShigaJapan College of Science and Engineering Ritsumeikan UniversityKusatsuShigaJapan

Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining Image Processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.

关键词： character recognition deep learning Image Processing Japanese books reorganization Kuzushiji