{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:40:32Z","timestamp":1767706832876,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100016365","name":"University of Pardubice","doi-asserted-by":"crossref","award":["IGA funds"],"award-info":[{"award-number":["IGA funds"]}],"id":[{"id":"10.13039\/501100016365","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The application of protective layers is the primary method of keeping metallic structures resistant to degradation. The measurement of the layer resistance to delamination is one of the important indicators of the protection quality. Therefore, ISO 4628 standard has been issued to handle and quantify the main coating defects. Here, an innovative assessment of degree of delamination around a scribe according to ISO 4628 standard has been practically realized. It utilizes an computer-driven deep learning-based method. The assessment method is composed of two shallow U-shaped convolutional networks in a row; the first for preliminary and the second for refined detection of delamination area around a scribe. The experiments performed on 586 samples showed that the proposed sequence of U-shaped convolutional networks meets the edge computing standards, provides good generalization capability, and provides precise delamination area detection for a large variability of surfaces.<\/jats:p>","DOI":"10.1007\/s44196-022-00141-1","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T12:02:36Z","timestamp":1662984156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Sequence of U-Shaped Convolutional Networks for Assessment of Degree of Delamination Around Scribe"],"prefix":"10.1007","volume":"15","author":[{"given":"Veronika","family":"Rozsivalova","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7359-0764","authenticated-orcid":false,"given":"Petr","family":"Dolezel","sequence":"additional","affiliation":[]},{"given":"Dominik","family":"Stursa","sequence":"additional","affiliation":[]},{"given":"Pavel","family":"Rozsival","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"issue":"12","key":"141_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"141_CR2","doi-asserted-by":"publisher","unstructured":"Bastian, B., Ranjith, S., et al.: Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDT E Int. ,(2019). https:\/\/doi.org\/10.1016\/j.ndteint.2019.102134","DOI":"10.1016\/j.ndteint.2019.102134"},{"issue":"2","key":"141_CR3","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.porgcoat.2009.01.002","volume":"65","author":"A Bastos","year":"2009","unstructured":"Bastos, A., Sim\u00f5es, A.: Effect of deep drawing on the performance of coil-coatings assessed by electrochemical techniques. Prog. Org. Coat. 65(2), 295\u2013303 (2009). https:\/\/doi.org\/10.1016\/j.porgcoat.2009.01.002","journal-title":"Prog. Org. Coat."},{"key":"141_CR4","doi-asserted-by":"publisher","unstructured":"Beheshti, N., Johnsson, L.: Squeeze u-net: a memory and energy efficient image segmentation network. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 1495\u20131504. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00190 (2020)","DOI":"10.1109\/CVPRW50498.2020.00190"},{"key":"141_CR5","doi-asserted-by":"publisher","unstructured":"Blanchard, P., Hill, D., Bretz, G., et\u00a0al.: Evaluation of corrosion protection methods for magnesium alloys in automotive applications, vol 9781118858943. https:\/\/doi.org\/10.1002\/9781118859803.ch94 (2014)","DOI":"10.1002\/9781118859803.ch94"},{"issue":"2","key":"141_CR6","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266\u2013277 (2001). https:\/\/doi.org\/10.1109\/83.902291","journal-title":"IEEE Trans. Image Process."},{"key":"141_CR7","doi-asserted-by":"publisher","unstructured":"Cringasu, E.C., Dragomirescu, A., Safta, C.A.: Image processing approach for estimating the degree of surface degradation by corrosion. In: 2017 International Conference on Energy and Environment (CIEM), pp 275\u2013278 (2017). https:\/\/doi.org\/10.1109\/CIEM.2017.8120791","DOI":"10.1109\/CIEM.2017.8120791"},{"key":"141_CR8","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/978-3-030-01054-6_16","volume":"868","author":"P Dolezel","year":"2018","unstructured":"Dolezel, P., Rozsival, P., Rozsivalova, V., et al.: Automatized approach to assessment of degree of delamination around a scribe. Adv. Intell. Syst. Comput. 868, 227\u2013236 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01054-6_16","journal-title":"Adv. Intell. Syst. Comput."},{"key":"141_CR9","doi-asserted-by":"publisher","unstructured":"Hanus, J.: Selection and evaluation of singlelayer coating compositions in corrosive environments. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59(5):53\u201364 (2011). https:\/\/doi.org\/10.11118\/actaun201159050053","DOI":"10.11118\/actaun201159050053"},{"key":"141_CR10","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., et\u00a0al.: Deep residual learning for image recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"141_CR11","unstructured":"Howard, A.G., Zhu, M., Chen, B., et\u00a0al.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017). arxiv:1704.04861"},{"issue":"8","key":"141_CR12","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s35144-019-0274-3","volume":"59","author":"AS Jandel","year":"2019","unstructured":"Jandel, A.S.: Sustainability in the coil coatings sector. JOT Journal fuer Oberflaechentechnik 59(8), 10\u201313 (2019). https:\/\/doi.org\/10.1007\/s35144-019-0274-3","journal-title":"JOT Journal fuer Oberflaechentechnik"},{"key":"141_CR13","doi-asserted-by":"publisher","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., et\u00a0al.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 1175\u20131183 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.156","DOI":"10.1109\/CVPRW.2017.156"},{"issue":"12","key":"141_CR14","doi-asserted-by":"publisher","first-page":"4415","DOI":"10.1016\/j.corsci.2007.03.049","volume":"49","author":"P Kapsalas","year":"2007","unstructured":"Kapsalas, P., Zervakis, M., Maravelaki-Kalaitzaki, P.: Evaluation of image segmentation approaches for non-destructive detection and quantification of corrosion damage on stonework. Corros. Sci. 49(12), 4415\u20134442 (2007). https:\/\/doi.org\/10.1016\/j.corsci.2007.03.049","journal-title":"Corros. Sci."},{"key":"141_CR15","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3431\u20133440 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"6","key":"141_CR16","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1007\/s11668-018-0528-z","volume":"18","author":"A Maples","year":"2018","unstructured":"Maples, A., Williams, E., Rawlins, J.: Understanding scribe profile and tool type effects on visual corrosion assessments. J. Fail. Anal. Prev. 18(6), 1401\u20131410 (2018). https:\/\/doi.org\/10.1007\/s11668-018-0528-z","journal-title":"J. Fail. Anal. Prev."},{"key":"141_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Boykov, Y., Porikli, F., et al.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"141_CR18","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., et\u00a0al.: Attention u-net: learning where to look for the pancreas (2018). arxiv:1804.03999"},{"issue":"2","key":"141_CR19","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.net.2020.07.020","volume":"53","author":"T Papamarkou","year":"2021","unstructured":"Papamarkou, T., Guy, H., Kroencke, B., et al.: Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks. Nucl. Eng. Technol. 53(2), 657\u2013665 (2021). https:\/\/doi.org\/10.1016\/j.net.2020.07.020","journal-title":"Nucl. Eng. Technol."},{"key":"141_CR20","doi-asserted-by":"publisher","unstructured":"Rahman, A., Wu, Z., Kalfarisi, R.: Semantic deep learning integrated with RGB feature-based rule optimization for facility surface corrosion detection and evaluation. J. Comput. Civ. Eng. (2021). https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000982","DOI":"10.1061\/(ASCE)CP.1943-5487.0000982"},{"key":"141_CR21","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. Lecture Notes Comput. Sci. 9351, 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"16\u201317","key":"141_CR22","doi-asserted-by":"publisher","first-page":"7416","DOI":"10.1016\/j.surfcoat.2007.02.008","volume":"201","author":"S Rossi","year":"2007","unstructured":"Rossi, S., Deflorian, F., Fiorenza, J.: Environmental influences on the abrasion resistance of a coil coating system. Surf. Coat. Technol. 201(16\u201317), 7416\u20137424 (2007). https:\/\/doi.org\/10.1016\/j.surfcoat.2007.02.008","journal-title":"Surf. Coat. Technol."},{"key":"141_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)"},{"key":"141_CR24","unstructured":"The National Coil Coating Association: NCCA promotes growth of coil coating industry. Light Metal Age 78(5), 66\u201367 (2020)"},{"key":"141_CR25","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.corsci.2014.07.045","volume":"88","author":"M Whitfield","year":"2014","unstructured":"Whitfield, M., Bono, D., Wei, L., et al.: High-throughput corrosion quantification in varied microenvironments. Corros. Sci. 88, 481\u2013486 (2014). https:\/\/doi.org\/10.1016\/j.corsci.2014.07.045","journal-title":"Corros. Sci."},{"key":"141_CR26","doi-asserted-by":"publisher","unstructured":"Yu, C., Wang, J., Peng, C., et\u00a0al.: Bisenet: bilateral segmentation network for real-time semantic segmentation. Lecture Notes Comput. Sci. 11217, LNCS:334\u2013349. https:\/\/doi.org\/10.1007\/978-3-030-01261-8_20 (2018)","DOI":"10.1007\/978-3-030-01261-8_20"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00141-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-022-00141-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00141-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T12:12:01Z","timestamp":1662984721000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-022-00141-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["141"],"URL":"https:\/\/doi.org\/10.1007\/s44196-022-00141-1","relation":{},"ISSN":["1875-6883"],"issn-type":[{"type":"electronic","value":"1875-6883"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"23 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"76"}}