{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T08:30:18Z","timestamp":1744619418368},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Industrial Artificial Intelligence"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Detection and measurement of cracks in asphalt pavement is one of the important tasks in transport industry to determine the quality of the pavement and submit repair requirements. In recent years, computer vision algorithms have been increasingly used to automate the solution of this problem. Therefore, researchers are faced with the acute issue of improving the accuracy of segmentation algorithms, since the safety of people depends on the timely detection of defects on the road. In this paper, ensemble methods based on Choquet and Sugeno fuzzy integrals are proposed to combine the scores of three pre-trained deep learning models: ResNet50, DenseNet169, and InceptionV3. We tested the proposed methods on a public dataset and compared the results with already-used popular ensemble methods.<\/jats:p>","DOI":"10.1007\/s44244-023-00008-0","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:03:36Z","timestamp":1679011416000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using the fuzzy integrals for the ensemble-based segmentation of asphalt cracks"],"prefix":"10.1007","volume":"1","author":[{"given":"Gleb","family":"Cyganov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artem","family":"Rychenkov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandr","family":"Sinitca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitrii","family":"Kaplun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"issue":"6","key":"8_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s42254-022-00455-1","volume":"4","author":"G Karagiorgi","year":"2022","unstructured":"Karagiorgi G, Kasieczka G, Kravitz S, Nachman B, Shih D (2022) Machine learning in the search for new fundamental physics. Nat Rev Phys 4(6):399\u2013412","journal-title":"Nat Rev Phys"},{"issue":"11","key":"8_CR2","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1038\/s42256-021-00399-8","volume":"3","author":"R Shad","year":"2021","unstructured":"Shad R, Cunningham JP, Ashley EA, Langlotz CP, Hiesinger W (2021) Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat Mach Intell 3(11):929\u2013935","journal-title":"Nat Mach Intell"},{"issue":"4","key":"8_CR3","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s11831-019-09344-w","volume":"27","author":"S Dargan","year":"2020","unstructured":"Dargan S, Kumar M, Ayyagari MR, Kumar G (2020) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071\u20131092","journal-title":"Arch Comput Methods Eng"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. In: IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"168175","DOI":"10.1109\/ACCESS.2019.2912908","volume":"7","author":"S Xia","year":"2019","unstructured":"Xia S, Xia Y, Yu H, Liu Q, Luo Y, Wang G, Chen Z (2019) Transferring ensemble representations using deep convolutional neural networks for small-scale image classification. IEEE Access 7:168175\u2013168186","journal-title":"IEEE Access"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"M\u00fcller D, Soto-Rey I, Kramer F (2022) An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks. arXiv preprint arXiv:2201.11440","DOI":"10.1109\/ACCESS.2022.3182399"},{"key":"8_CR7","unstructured":"Kundu R, Basak H, Koilada A, Chattopadhyay S, Chakraborty S, Das N (2021) Ensemble of cnn classifiers using sugeno fuzzy integral technique for cervical cytology image classification. arXiv preprint arXiv:2108.09460 (2021)"},{"key":"8_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104895","volume":"138","author":"R Kundu","year":"2021","unstructured":"Kundu R, Singh PK, Mirjalili S, Sarkar R (2021) Covid-19 detection from lung CT-scans using a fuzzy integral-based CNN ensemble. Comput Biol Med 138:104895","journal-title":"Comput Biol Med"},{"issue":"5","key":"8_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/coatings7050065","volume":"7","author":"G Loprencipe","year":"2017","unstructured":"Loprencipe G, Pantuso A (2017) A specified procedure for distress identification and assessment for urban road surfaces based on PCI. Coatings 7(5):65","journal-title":"Coatings"},{"issue":"3","key":"8_CR10","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TIP.2018.2878966","volume":"28","author":"Q Zou","year":"2018","unstructured":"Zou Q, Zhang Z, Li Q, Qi X, Wang Q, Wang S (2018) Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Trans Image Process 28(3):1498\u20131512","journal-title":"IEEE Trans Image Process"},{"key":"8_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103176","volume":"114","author":"A Ji","year":"2020","unstructured":"Ji A, Xue X, Wang Y, Luo X, Xue W (2020) An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Autom Constr 114:103176","journal-title":"Autom Constr"},{"issue":"10","key":"8_CR12","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1111\/mice.12297","volume":"32","author":"A Zhang","year":"2017","unstructured":"Zhang A, Wang KC, Li B, Yang E, Dai X, Peng Y, Fei Y, Liu Y, Li JQ, Chen C (2017) Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network. Comput Aided Civil Infrastruct Eng 32(10):805\u2013819","journal-title":"Comput Aided Civil Infrastruct Eng"},{"issue":"3","key":"8_CR13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1111\/mice.12409","volume":"34","author":"A Zhang","year":"2019","unstructured":"Zhang A, Wang KC, Fei Y, Liu Y, Chen C, Yang G, Li JQ, Yang E, Qiu S (2019) Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network. Comput Aided Civil Infrastruct Eng 34(3):213\u2013229","journal-title":"Comput Aided Civil Infrastruct Eng"},{"issue":"2","key":"8_CR14","doi-asserted-by":"publisher","first-page":"152","DOI":"10.3390\/coatings10020152","volume":"10","author":"Z Fan","year":"2020","unstructured":"Fan Z, Li C, Chen Y, Di Mascio P, Chen X, Zhu G, Loprencipe G (2020) Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. Coatings 10(2):152","journal-title":"Coatings"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","volume":"21","author":"F Yang","year":"2019","unstructured":"Yang F, Zhang L, Yu S, Prokhorov D, Mei X, Ling H (2019) Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans Intell Transp Syst 21(4):1525\u20131535","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8_CR17","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Chaurasia A, Culurciello E (2017) Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134 . IEEE","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Wu M, Shu Z, Zhang J, Hu X (2021) Hrlinknet: Linknet with high-resolution representation for high-resolution satellite imagery. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 2504\u20132507. IEEE","DOI":"10.1109\/IGARSS47720.2021.9554601"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/B978-1-4832-1450-4.50027-4","volume-title":"Readings in fuzzy sets for intelligent systems","author":"M Sugeno","year":"1993","unstructured":"Sugeno M (1993) Fuzzy measures and fuzzy integrals\u2014a survey. Readings in fuzzy sets for intelligent systems. Elsevier, Amsterdam, pp 251\u2013257"},{"issue":"2","key":"8_CR24","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/0165-0114(89)90194-2","volume":"29","author":"T Murofushi","year":"1989","unstructured":"Murofushi T, Sugeno M (1989) An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Syst 29(2):201\u2013227","journal-title":"Fuzzy Sets Syst"},{"issue":"3","key":"8_CR25","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1109\/21.57289","volume":"20","author":"H Tahani","year":"1990","unstructured":"Tahani H, Keller JM (1990) Information fusion in computer vision using the fuzzy integral. IEEE Trans Syst Man Cybern 20(3):733\u2013741","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"114892","DOI":"10.1109\/ACCESS.2020.3003638","volume":"8","author":"SL Lau","year":"2020","unstructured":"Lau SL, Chong EK, Yang X, Wang X (2020) Automated pavement crack segmentation using u-net-based convolutional neural network. IEEE Access 8:114892\u2013114899","journal-title":"IEEE Access"},{"key":"8_CR27","unstructured":"Jenkins MD, Carr TA, Iglesias MI, Buggy T, Morison G (2018) A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. In: 2018 26th European Signal Processing Conference (EUSIPCO), pp. 2120\u20132124. IEEE"},{"key":"8_CR28","unstructured":"Fan Z, Wu Y, Lu J, Li W (2018) Automatic pavement crack detection based on structured prediction with the convolutional neural network. arXiv preprint arXiv:1802.02208"},{"key":"8_CR29","unstructured":"Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, Sun Y, He T, Mueller J, Manmatha R et al (2020) Resnest: split-attention networks. arXiv e-prints, 2004"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Zhang L, Yang F, Zhang YD, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: Image Processing (ICIP), 2016 IEEE International Conference On, pp. 3708\u20133712. IEEE","DOI":"10.1109\/ICIP.2016.7533052"}],"container-title":["Industrial Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44244-023-00008-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44244-023-00008-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44244-023-00008-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:09:17Z","timestamp":1679011757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44244-023-00008-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["8"],"URL":"https:\/\/doi.org\/10.1007\/s44244-023-00008-0","relation":{},"ISSN":["2731-667X"],"issn-type":[{"value":"2731-667X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,17]]},"assertion":[{"value":"3 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5"}}