{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:59:04Z","timestamp":1772769544076,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mistra, the Swedish Foundation for Strategic Environmental Research, and Stockholms Stadshus AB"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method\u2019s performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods.<\/jats:p>","DOI":"10.3390\/s24092736","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T08:08:32Z","timestamp":1714032512000},"page":"2736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Image-Based Concrete Crack Detection Method Using the Median Absolute Deviation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2220-2770","authenticated-orcid":false,"given":"Juan Camilo","family":"Avenda\u00f1o","sequence":"first","affiliation":[{"name":"Division of Structural Engineering and Bridges, KTH Royal Institute of Technology, 10044 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2833-4585","authenticated-orcid":false,"given":"John","family":"Leander","sequence":"additional","affiliation":[{"name":"Division of Structural Engineering and Bridges, KTH Royal Institute of Technology, 10044 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5447-2068","authenticated-orcid":false,"given":"Raid","family":"Karoumi","sequence":"additional","affiliation":[{"name":"Division of Structural Engineering and Bridges, KTH Royal Institute of Technology, 10044 Stockholm, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s13349-017-0252-5","article-title":"Structural health monitoring of bridges: A model-free ANN-based approach to damage detection","volume":"7","author":"Neves","year":"2017","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105478","DOI":"10.1016\/j.engappai.2022.105478","article-title":"Computer vision framework for crack detection of civil infrastructure\u2014A review","volume":"117","author":"Ai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sonbul, O.S., and Rashid, M. (2023). Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review. Sensors, 23.","DOI":"10.3390\/s23094230"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.aei.2015.01.008","article-title":"A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure","volume":"29","author":"Koch","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1080\/15732479.2011.654124","article-title":"Life-cycle social analysis of motorway bridges","volume":"9","year":"2013","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1260\/1369-4332.17.3.289","article-title":"Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures","volume":"17","author":"Morgenthal","year":"2014","journal-title":"Adv. Struct. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1111\/mice.12141","article-title":"Vision-Based Automated Crack Detection for Bridge Inspection","volume":"30","author":"Yeum","year":"2015","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"04014124","DOI":"10.1061\/(ASCE)CP.1943-5487.0000446","article-title":"Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction","volume":"30","author":"Liu","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Neves, A.C. (2017). Structural Health Monitoring of Bridges: Model-Free Damage Detection Method Using Machine Learning, KTH Royal Institute of Technology.","DOI":"10.1007\/s13349-017-0252-5"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e2321","DOI":"10.1002\/stc.2321","article-title":"A literature review of next-generation smart sensing technology in structural health monitoring","volume":"26","author":"Sony","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"015019","DOI":"10.1088\/0964-1726\/19\/1\/015019","article-title":"Integration of computer imaging and sensor data for structural health monitoring of bridges","volume":"19","author":"Zaurin","year":"2010","journal-title":"Smart Mater. Struct."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1061\/JHTRCQ.0000435","article-title":"Review on Automatic Pavement Crack Image Recognition Algorithms","volume":"9","author":"Peng","year":"2015","journal-title":"J. Highw. Transp. Res. Dev. (Engl. Ed.)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13640-017-0187-0","article-title":"Efficient pavement crack detection and classification","volume":"2017","author":"Villatoro","year":"2017","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Epshtein, B., Ofek, E., and Wexler, Y. (2010, January 13\u201318). Detecting text in natural scenes with stroke width transform. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540041"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., and Sim, S.H. (2017). Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing. Sensors, 17.","DOI":"10.3390\/s17092052"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qin, G., and Wang, X. (2010, January 16\u201318). Improvement of canny algorithm based on pavement edge detection. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5646923"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1061\/(ASCE)0887-3801(2006)20:3(210)","article-title":"Improved Image Analysis for Evaluating Concrete Damage","volume":"20","author":"Hutchinson","year":"2006","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_19","unstructured":"Kim, H., Sim, S.H., and Cho, S. (2015, January 1\u20132). Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing. Proceedings of the International Conference on Advances in Experimental Structural Engineering, Urbana, IL, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60100","DOI":"10.1109\/ACCESS.2018.2875889","article-title":"Image-Based Crack Detection Using Crack Width Transform (CWT) Algorithm","volume":"6","author":"Cho","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1061\/(ASCE)0733-947X(1992)118:5(700)","article-title":"Histogram-based approach for automated pavement-crack sensing","volume":"118","author":"Kirschke","year":"1992","journal-title":"J. Transp. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kapela, R., \u015aniata\u0142a, P., Turkot, A., Rybarczyk, A., Po\u017carycki, A., Rydzewski, P., Wycza\u0142ek, M., and B\u0142och, A. (2015, January 25\u201327). Asphalt surfaced pavement cracks detection based on histograms of oriented gradients. Proceedings of the 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES), Torun, Poland.","DOI":"10.1109\/MIXDES.2015.7208590"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Q., and Liu, X. (2008, January 27\u201330). Novel approach to pavement image segmentation based on neighboring difference histogram method. Proceedings of the 2008 Congress on Image and Signal Processing, Sanya, China.","DOI":"10.1109\/CISP.2008.13"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1007\/s11760-021-02123-w","article-title":"A potential crack region method to detect crack using image processing of multiple thresholding","volume":"16","author":"Chen","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14531","DOI":"10.1109\/ACCESS.2020.2966881","article-title":"Review of Pavement Defect Detection Methods","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"04018080","DOI":"10.1061\/(ASCE)BE.1943-5592.0001289","article-title":"Bridge Deterioration Quantification Protocol Using UAV","volume":"23","author":"Duque","year":"2018","journal-title":"J. Bridge Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dorafshan, S., Thomas, R.J., and Maguire, M. (2019). Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures. Infrastructures, 4.","DOI":"10.3390\/infrastructures4020019"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123549","DOI":"10.1016\/j.conbuildmat.2021.123549","article-title":"Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques","volume":"293","author":"Miao","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Munawar, H.S., Hammad, A.W.A., Haddad, A., Soares, C.A.P., and Waller, S.T. (2021). Image-based crack detection methods: A review. Infrastructures, 6.","DOI":"10.3390\/infrastructures6080115"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"\u00d6zgenel, F., and Sorgu\u00e7, A.G. (2018, January 20\u201325). Performance comparison of pretrained convolutional neural networks on crack detection in buildings. Proceedings of the ISARC 2018\u201435th International Symposium on Automation and Robotics in Construction and International AEC\/FM Hackathon: The Future of Building Things, Berlin, Germany.","DOI":"10.22260\/ISARC2018\/0094"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"04019128","DOI":"10.1061\/(ASCE)BE.1943-5592.0001507","article-title":"Benchmark for Evaluating Performance in Visual Inspection of Fatigue Cracking in Steel Bridges","volume":"25","author":"Campbell","year":"2020","journal-title":"J. Bridge Eng."},{"key":"ref_32","first-page":"199","article-title":"Probability of detection as a metric for quantifying NDE capability: The state of the art","volume":"14","author":"Keprate","year":"2015","journal-title":"J. Pipeline Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/mice.12564","article-title":"Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine","volume":"36","author":"Chun","year":"2021","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s11760-015-0828-7","article-title":"Optimizing the color-to-grayscale conversion for image classification","volume":"10","author":"Kalkan","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","article-title":"Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median","volume":"49","author":"Leys","year":"2013","journal-title":"J. Exp. Soc. Psychol."},{"key":"ref_36","unstructured":"Avenda\u00f1o, J.C., Leander, J., and Karoumi, R. (2022). Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, CRC Press."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Park, K., Song, Y., and Cheong, Y.G. (2018, January 26\u201329). Classification of attack types for intrusion detection systems using a machine learning algorithm. Proceedings of the 2018 IEEE 4th International Conference on Big Data Computing Service and Applications (BigDataService), Bamberg, Germany.","DOI":"10.1109\/BigDataService.2018.00050"},{"key":"ref_38","first-page":"838","article-title":"Precision-Recall-Gain curves: PR analysis done right","volume":"1","author":"Flach","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Pearson. [3rd ed.]."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"129238","DOI":"10.1016\/j.conbuildmat.2022.129238","article-title":"Review on computer vision-based crack detection and quantification methodologies for civil structures","volume":"356","author":"Deng","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.matpr.2022.11.356","article-title":"Concrete bridge crack detection by image processing technique by using the improved OTSU method","volume":"74","author":"Vivekananthan","year":"2023","journal-title":"Mater. 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