{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T13:13:07Z","timestamp":1769605987854,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T00:00:00Z","timestamp":1636243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1G1A1095215, NRF-2018R1A5A1025137"],"award-info":[{"award-number":["2019R1G1A1095215, NRF-2018R1A5A1025137"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure\u2019s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object\u2019s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.<\/jats:p>","DOI":"10.3390\/s21217396","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"7396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multivariate Analysis of Concrete Image Using Thermography and Edge Detection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4226-7435","authenticated-orcid":false,"given":"Bubryur","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}]},{"given":"Se-Woon","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Architectural Engineering, Daegu Catholic University, Hayang-ro 13-13, Hayang-eup, Gyeongasan-si 38430, Korea"}]},{"given":"Gang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-3836","authenticated-orcid":false,"given":"Dong-Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Environment and Energy Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8551-8057","authenticated-orcid":false,"given":"Ronnie O.","family":"Serfa Juan","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Environment and Energy Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, B., Yuvaraj, N., Preethaa, K.S., Hu, G., and Lee, D.-E. (2021). Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network. Sensors, 21.","DOI":"10.3390\/s21072515"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.aei.2018.05.004","article-title":"Detecting healthy concrete surfaces","volume":"37","author":"Brilakis","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"04020028","DOI":"10.1061\/(ASCE)CP.1943-5487.0000907","article-title":"Deep Learning\u2013Based Enhancement of Motion Blurred UAV Concrete Crack Images","volume":"34","author":"Liu","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jweia.2021.104629","article-title":"Pressure pattern recognition in buildings using an unsupervised machine-learning algorithm","volume":"214","author":"Kim","year":"2021","journal-title":"J. Wind. Eng. Ind. Aerodyn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Kim, B., and Lee, D.E. (2021). Aerodynamic characteristics and lateral displacements of a set of two buildings in a linked tall building system. Sensors, 21.","DOI":"10.3390\/s21124046"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engstruct.2020.111275","article-title":"Multi-linear tensile stress-crack width relationship s for hybrid fibre reinforced concrete using inverse analysis and digital image correlation","volume":"225","author":"Bhosale","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1016\/j.autcon.2010.06.008","article-title":"Parameter optimization for automated concrete detection in image data","volume":"19","author":"Zhu","year":"2010","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.ndteint.2008.09.003","article-title":"Imaging of internal cracks in concrete structures using the surface rendering technique","volume":"42","author":"Yeh","year":"2009","journal-title":"NDT E Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.autcon.2017.06.024","article-title":"Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography","volume":"83","author":"Omar","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.measurement.2017.05.051","article-title":"Concrete compressive strength detection using image processing based new test method","volume":"109","author":"Dogan","year":"2017","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TCSVT.2020.3028008","article-title":"Multi-deformation aware attention learning for concrete structural defect classification","volume":"31","author":"Bhattacharya","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.conbuildmat.2012.07.055","article-title":"Assessment of concrete compressive strength by image processing technique","volume":"37","year":"2012","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107474","DOI":"10.1016\/j.patcog.2020.107474","article-title":"A novel hybrid approach for crack detection","volume":"107","author":"Fang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ndteint.2010.04.007","article-title":"Imaging-based detection of AAR induced map-cracking damage in concrete structure","volume":"43","author":"Kabir","year":"2010","journal-title":"NDT E Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1111\/mice.12626","article-title":"Automated crack assessment and quantitative growth monitoring","volume":"36","author":"Kong","year":"2020","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103372","DOI":"10.1016\/j.autcon.2020.103372","article-title":"Artificial intelligence-empowered pipeline for image-based inspection of concrete structures","volume":"120","author":"Chow","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)CP.1943-5487.0000918","article-title":"Machine learning for crack detection: Review and model performance comparison","volume":"34","author":"Hsieh","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1016\/j.buildenv.2010.03.015","article-title":"Experimental study on microscopic moving characteristics of pedestrians in built corridor based on digital image processing","volume":"45","author":"Ma","year":"2010","journal-title":"Build. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103246","DOI":"10.1016\/j.autcon.2020.103246","article-title":"Intelligent rolling compaction system for earth-rock dams","volume":"116","author":"Zhang","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106118","DOI":"10.1016\/j.cemconres.2020.106118","article-title":"Deep learning-based automated image segmentation for concrete petrographic analysis","volume":"135","author":"Song","year":"2020","journal-title":"Cem. Concr. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1177\/1475921718811157","article-title":"Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing","volume":"18","author":"Jung","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijleo.2015.09.147","article-title":"Detection crack in image using Otsu method and multiple filtering in image processing techniques","volume":"127","author":"Talab","year":"2016","journal-title":"Optik"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1061\/(ASCE)0887-3801(2003)17:4(255)","article-title":"Analysis of Edge-Detection Techniques for Crack Identification in Bridges","volume":"17","author":"Abudayyeh","year":"2003","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.conbuildmat.2017.04.097","article-title":"Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks","volume":"146","author":"Tong","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9289","DOI":"10.1007\/s00521-021-05690-8","article-title":"Surface crack detection using deep learning with shallow CNN architecture for enhanced computation","volume":"33","author":"Kim","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17081","DOI":"10.1007\/s00500-020-04999-1","article-title":"Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance","volume":"24","author":"Kim","year":"2020","journal-title":"Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, B., Serfa Juan, R.O., Lee, D.-E., and Chen, Z. (2021). Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image. Appl. Sci., 11.","DOI":"10.3390\/app11188388"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.imavis.2019.03.006","article-title":"Image super resolution by dilated dense progressive network","volume":"88","author":"Shamsolmoali","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.imavis.2017.08.009","article-title":"A further study of low resolution androgenic hair patterns as a soft biometric trait","volume":"69","author":"Chan","year":"2018","journal-title":"Image Vis. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1006\/jmre.1997.1319","article-title":"Improved Resolution and Signal-to-Noise Ratio in MRI via Enhanced Signal Digitization","volume":"130","author":"Elliott","year":"1998","journal-title":"J. Magn. Reson."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/10298436.2011.561345","article-title":"A volumetrics thresholding algorithm for processing asphalt concrete X-ray CT images","volume":"12","author":"Zelelew","year":"2011","journal-title":"Int. J. Pavement Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/TIP.2003.812373","article-title":"Visualization of high dynamic range images","volume":"12","author":"Pardo","year":"2003","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, S., Dai, G., Wang, Z., Li, L., Wei, X., and Xie, Y. (2018). A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images. BMC Med. Imaging, 18.","DOI":"10.1186\/s12880-018-0256-6"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"119427","DOI":"10.1016\/j.conbuildmat.2020.119427","article-title":"Compressive strength determination of concrete specimens using X-ray computed tomography and finite element method","volume":"256","author":"Khormani","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.measurement.2018.12.105","article-title":"Real time video image enhancement approach using particle swarm optimisation technique with adaptive cumulative distribution function based histogram equalization","volume":"145","author":"Jasmine","year":"2019","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TIP.2009.2018014","article-title":"Image Quality Assessment Based on Multiscale Geometric Analysis","volume":"18","author":"Gao","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.1016\/j.conbuildmat.2009.06.013","article-title":"Damage assessment for concrete structure using image processing techniques on acoustic borehole imagery. Constr","volume":"23","author":"Kabir","year":"2009","journal-title":"Build. Mater."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bugarinovi\u0107, \u017d., Pajewski, L., Risti\u0107, A., Vrtunski, M., Govedarica, M., and Borisov, M. (2020). On the introduction of canny operator in an advanced imaging algorithm for real-time detection of hyperbolas in ground-penetrating radar data. Electronics, 9.","DOI":"10.3390\/electronics9030541"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:27:03Z","timestamp":1760167623000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,7]]},"references-count":39,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217396"],"URL":"https:\/\/doi.org\/10.3390\/s21217396","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,7]]}}}