{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:03:09Z","timestamp":1773658989380,"version":"3.50.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s12065-025-01130-x","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T07:14:21Z","timestamp":1768029261000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An enhanced approach to road damage classification with deep feature extraction and class-specific clustering"],"prefix":"10.1007","volume":"19","author":[{"given":"R.","family":"Rakshitha","sequence":"first","affiliation":[]},{"given":"S.","family":"Srinath","sequence":"additional","affiliation":[]},{"given":"N. Vinay","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"S.","family":"Rashmi","sequence":"additional","affiliation":[]},{"given":"B. V.","family":"Poornima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"1130_CR1","doi-asserted-by":"publisher","unstructured":"Ragnoli A et al (2018) Pavement distress detection methods: A review transportation safety and pavement management view project pavement distress detection methods: a review. https:\/\/doi.org\/10.20944\/preprints201809.0567.v1","DOI":"10.20944\/preprints201809.0567.v1"},{"issue":"1","key":"1130_CR2","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TITS.2012.2208630","volume":"14","author":"H Oliveira","year":"2012","unstructured":"Oliveira H, Correia PL (2012) Automatic road crack detection and characterization. IEEE Trans Intell Transp Syst 14(1):155\u2013168. https:\/\/doi.org\/10.1109\/TITS.2012.2208630","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"1130_CR3","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1177\/03611981211002203","volume":"2675","author":"N Safaei","year":"2021","unstructured":"Safaei N, Smadi O, Safaei B, Masoud A (2021) Efficient road crack detection based on an adaptive pixel-level segmentation algorithm. Transp Res Rec 2675(9):370\u2013381. https:\/\/doi.org\/10.1177\/03611981211002203","journal-title":"Transp Res Rec"},{"issue":"9","key":"1130_CR4","doi-asserted-by":"publisher","first-page":"3274","DOI":"10.1080\/10298436.2021.1888092","volume":"23","author":"C Chen","year":"2022","unstructured":"Chen C, Seo H, Jun CH, Zhao Y (2022) Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. Int J Pavement Eng 23(9):3274\u20133283. https:\/\/doi.org\/10.1080\/10298436.2021.1888092","journal-title":"Int J Pavement Eng"},{"key":"1130_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08738-0","author":"D-R Chen","year":"2023","unstructured":"Chen D-R, Chiu W-M (2023) Deep-learning-based road crack detection frameworks for dashcam-captured images under different illumination conditions. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-023-08738-0","journal-title":"Soft Comput"},{"key":"1130_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101182","volume":"46","author":"M-T Cao","year":"2020","unstructured":"Cao M-T, Tran Q-V, Nguyen N-M, Chang K-T (2020) Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Adv Eng Inform 46:101182. https:\/\/doi.org\/10.1016\/j.aei.2020.101182","journal-title":"Adv Eng Inform"},{"issue":"2","key":"1130_CR7","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.aei.2016.03.002","volume":"30","author":"C Koch","year":"2016","unstructured":"Koch C, Doycheva K, Kasireddy V, Akinci B, Fieguth P (2016) Erratum: a review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (Advanced Engineering Informatics (2015) 29:2 (196\u2013210)). Adv Eng Inform 30(2):208\u2013210. https:\/\/doi.org\/10.1016\/j.aei.2016.03.002","journal-title":"Adv Eng Inform"},{"key":"1130_CR8","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285\u20131298. https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Trans Med Imaging"},{"key":"1130_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","volume":"80","author":"J Lu","year":"2015","unstructured":"Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G (2015) Transfer learning using computational intelligence: a survey. Knowledge-Based Syst 80:14\u201323. https:\/\/doi.org\/10.1016\/j.knosys.2015.01.010","journal-title":"Knowledge-Based Syst"},{"issue":"13","key":"1130_CR10","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1080\/10298","volume":"22","author":"Y Du","year":"2020","unstructured":"Du Y, Pan N, Xu Z, Deng F, Shen Y, Kang H (2020) Pavement distress detection and classification based on YOLO network. Int J Pavement Eng 22(13):1659\u201372. https:\/\/doi.org\/10.1080\/10298","journal-title":"Int J Pavement Eng"},{"key":"1130_CR11","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and, Recognition P (2009) (CVPR), Miami, FL, USA, pp. 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1130_CR12","doi-asserted-by":"publisher","unstructured":"Arya D, Maeda H, Ghosh SK, Toshniwal D, Sekimoto Y (2024) RDD2022: a multi-national image dataset for automatic road damage detection. Geo-spatial Data 3(2). https:\/\/doi.org\/10.1002\/gdj3.260","DOI":"10.1002\/gdj3.260"},{"key":"1130_CR13","doi-asserted-by":"crossref","unstructured":"Lim RS, La HM, Shan Z, Sheng W (2011) Developing a crack inspection robot for bridge maintenance. In: Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 9\u201313 May 2011; IEEE: Piscataway, pp. 6288\u20136293. [Google Scholar]","DOI":"10.1109\/ICRA.2011.5980131"},{"key":"1130_CR14","doi-asserted-by":"crossref","unstructured":"Kapela R, \u015aniata\u0142a P, Turkot A, Rybarczyk A, Po\u017carycki A, Rydzewski P, Wycza\u0142ek M, B\u0142och A (2015) Asphalt surfaced pavement cracks detection based on histograms of oriented gradients. In: Proceedings of the 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES), Torun, Poland, pp. 579\u2013584. [Google Scholar]","DOI":"10.1109\/MIXDES.2015.7208590"},{"key":"1130_CR15","first-page":"28","volume":"29","author":"G-L Wang","year":"2010","unstructured":"Wang G-L, Hu J, Qian J-G, Wang Y-Q (2010) Simulation in time domain for nonstationary road disturbances and its wavelet analysis. Zhendong Yu Chongji (J. Vib. Shock) 29:28\u201332","journal-title":"Zhendong Yu Chongji (J. Vib. Shock)"},{"key":"#cr-split#-1130_CR16.1","doi-asserted-by":"crossref","unstructured":"Salman M, Mathavan S, Kamal K, Rahman M (2013) Pavement crack detection using the Gabor flter. In: Salman M","DOI":"10.1109\/ITSC.2013.6728529"},{"key":"#cr-split#-1130_CR16.2","unstructured":"(ed) 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) 2039-2044 (IEEE)"},{"key":"#cr-split#-1130_CR17.1","doi-asserted-by":"crossref","unstructured":"Niu B, Wu H, Meng Y et al (2020) Application of cem algorithm in the feld of tunnel crack identifcation. In: Niu B","DOI":"10.1109\/ICIVC50857.2020.9177491"},{"key":"#cr-split#-1130_CR17.2","unstructured":"(ed) 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), IEEE, pp. 232-236"},{"key":"1130_CR18","doi-asserted-by":"crossref","unstructured":"Baltazart V, Nicolle P, Yang L (2017) Ongoing tests and improvements of the mps algorithm for the automatic crack detection within grey level pavement images. In: Baltazart V (eds) 2017 25th European Signal Processing Conference (EUSIPCO) (2016\u20132020). IEEE","DOI":"10.23919\/EUSIPCO.2017.8081563"},{"issue":"2","key":"1130_CR19","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1080\/15732479.2019.1655068","volume":"16","author":"J Jo","year":"2020","unstructured":"Jo J, Jadidi Z (2020) A high precision crack classifcation system using multi-layered image processing and deep belief learning. Struct Infrastruct Eng 16(2):297\u2013305","journal-title":"Struct Infrastruct Eng"},{"key":"1130_CR20","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s41062-023-01315-2","volume":"9","author":"HB Ibrahim","year":"2024","unstructured":"Ibrahim HB, Salah M, Zarzoura F et al (2024) Smart monitoring of road pavement deformations from UAV images by using machine learning. Innov Infrastruct Solut 9:16. https:\/\/doi.org\/10.1007\/s41062-023-01315-2","journal-title":"Innov Infrastruct Solut"},{"issue":"4","key":"1130_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/a13040081","volume":"13","author":"FG Pratico","year":"2020","unstructured":"Pratico FG, Fedele R, Naumov V, Sauer T (2020) Detection and monitoring of bottom-up cracks in road pavement using a machinelearning approach. Algorithms 13(4):81","journal-title":"Algorithms"},{"issue":"4","key":"1130_CR22","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.dcan.2021.03.003","volume":"7","author":"L Zhang","year":"2021","unstructured":"Zhang L et al (2021) Machine learning-based real-time visible fatigue crack growth detection. Digit Commun Networks 7(4):551\u2013558","journal-title":"Digit Commun Networks"},{"key":"1130_CR23","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7419058","volume":"2018","author":"ND Hoang","year":"2018","unstructured":"Hoang ND (2018) An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Adv Civil Eng 2018:7419058. https:\/\/doi.org\/10.1155\/2018\/7419058","journal-title":"Adv Civil Eng"},{"key":"1130_CR24","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.aei.2019.04.004","volume":"40","author":"N-D Hoang","year":"2019","unstructured":"Hoang N-D (2019) Image processing-based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach. Adv Eng Inform 40:110\u2013120. https:\/\/doi.org\/10.1016\/j.aei.2019.04.004","journal-title":"Adv Eng Inform"},{"issue":"12","key":"1130_CR25","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1111\/mice.12387","volume":"33","author":"H Maeda","year":"2018","unstructured":"Maeda H, Sekimoto Y, Seto T, Kashiyama T, Omata H (2018) Road damage detection and classification using deep neural networks with smartphone images. Comput Aided Civil Infrastruct Eng 33(12):1127\u20131141. https:\/\/doi.org\/10.1111\/mice.12387","journal-title":"Comput Aided Civil Infrastruct Eng"},{"key":"1130_CR26","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12561","author":"H Maeda","year":"2020","unstructured":"Maeda H, Kashiyama T, Sekimoto Y, Seto T, Omata H (2020) Generative adversarial network for road damage detection. Comput Aided Civ Infrastruct Eng. https:\/\/doi.org\/10.1111\/mice.12561","journal-title":"Comput Aided Civ Infrastruct Eng"},{"key":"1130_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2021.107133","volume":"36","author":"D Arya","year":"2021","unstructured":"Arya D, Maeda H, Ghosh SK, Toshniwal D, Sekimoto Y (2021) RDD2020: an annotated image dataset for automatic road damage detection using deep learning. Data Brief 36:107133. https:\/\/doi.org\/10.1016\/j.dib.2021.107133","journal-title":"Data Brief"},{"key":"1130_CR28","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580\u2013587. [Google Scholar]","DOI":"10.1109\/CVPR.2014.81"},{"key":"1130_CR29","doi-asserted-by":"crossref","unstructured":"Girshick R, Fast R-CNN (2015) Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1440\u20131448. [Google Scholar]","DOI":"10.1109\/ICCV.2015.169"},{"key":"1130_CR30","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39:1137\u20131149","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1130_CR31","first-page":"1094","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. ECCV Trans Pattern Anal Mach Intell 37:1094\u20131916","journal-title":"ECCV Trans Pattern Anal Mach Intell"},{"key":"1130_CR32","first-page":"2201","volume":"65","author":"Q Chen","year":"2020","unstructured":"Chen Q, Gan X, Huang W, Feng J, Shim H (2020) Road damage detection and classification using mask R-CNN with densenet backbone. Comput Mater Contin 65:2201\u20132215","journal-title":"Comput Mater Contin"},{"key":"1130_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104613","volume":"144","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Zuo Z, Xu X, Wu J, Zhu J, Zhang H, Wang J, Tian Y (2022) Road damage detection using UAV images based on multi-level attention mechanism. Autom Constr 144:104613","journal-title":"Autom Constr"},{"key":"1130_CR34","doi-asserted-by":"crossref","unstructured":"Wang J, Gao X, Liu Z, Wan Y (2023) GSC-YOLOv5: an algorithm based on improved attention mechanism for road creak detection. In: Proceedings of the 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China. IEEE: Washington, DC, pp. 1664\u20131671","DOI":"10.1109\/DDCLS58216.2023.10166944"},{"key":"1130_CR35","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-022-00931-x","author":"F Wan","year":"2022","unstructured":"Wan F, Sun C, He H, Lei G, Xu L, Xiao T (2022) YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s. EURASIP J Adv Signal Process. https:\/\/doi.org\/10.1186\/s13634-022-00931-x","journal-title":"EURASIP J Adv Signal Process"},{"key":"1130_CR36","doi-asserted-by":"crossref","unstructured":"Mandal V, Mussah AR, Adu-Gyamfi Y (2020) Deep learning frameworks for pavement distress classification: a comparative analysis. In: Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, pp. 5577\u20135583","DOI":"10.1109\/BigData50022.2020.9378047"},{"key":"1130_CR37","doi-asserted-by":"crossref","unstructured":"Naddaf-Sh S et al (2020) IEEE. An efcient and scalable deep learning approach for road damage detection. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 5602\u20135608","DOI":"10.1109\/BigData50022.2020.9377751"},{"key":"1130_CR38","doi-asserted-by":"publisher","first-page":"109316","DOI":"10.1016\/j.measurement.2021.109316","volume":"178","author":"Y Xu","year":"2021","unstructured":"Xu Y, Li D, Xie Q, Wu Q, Wang J (2021) Automatic defect detection and segmentation of tunnel surface using modified mask R-CNN. Measurement 178:109316","journal-title":"Measurement"},{"key":"1130_CR39","doi-asserted-by":"crossref","unstructured":"Wang W, Wu B, Yang S, Wang Z (2018) Road damage detection and classification with faster R-CNN. In: Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA","DOI":"10.1109\/BigData.2018.8622354"},{"key":"1130_CR40","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/TITS.2020.2990703","volume":"22","author":"K Zhang","year":"2020","unstructured":"Zhang K, Zhang Y, Cheng HD (2020) CrackGAN: pavement crack detection using partially accurate ground truths based on generative adversarial learning. IEEE Trans. Intell. Transp. Syst. 22:1306\u20131319","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1130_CR41","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1111\/mice.12701","volume":"37","author":"Z Wang","year":"2022","unstructured":"Wang Z, Zhang Y, Mosalam KM, Gao Y, Huang S (2022) Deep semantic segmentation for visual understanding on construction sites. Comput -Aided Civ Infrastruct Eng 37:145\u2013162","journal-title":"Comput -Aided Civ Infrastruct Eng"},{"key":"1130_CR42","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1111\/mice.12412","volume":"33","author":"X Yang","year":"2018","unstructured":"Yang X, Li H, Yu Y, Luo X, Huang T, Yang X (2018) Automatic pixel-level crack detection and measurement using fully convolutional network. Comput.-Aided Civ. Infrastruct. Eng. 33:1090\u20131109","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"1130_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103833","volume":"130","author":"S Shim","year":"2020","unstructured":"Shim S, Kim J, Lee S-W, Cho G-C (2020) Road surface damage detection based on hierarchical architecture using lightweight auto-encoder network. Autom Constr 130:103833. https:\/\/doi.org\/10.1016\/j.autcon.2021.103833","journal-title":"Autom Constr"},{"key":"1130_CR44","unstructured":"Sheta AF, Turabieh H, Aljahdali S, Alangari A (2020) Pavement crack detection using convolutional neural network. In: Proceedings of the Computers and Their Applications, San Francisco"},{"key":"1130_CR45","doi-asserted-by":"publisher","first-page":"22166","DOI":"10.1109\/TITS.2022.3161960","volume":"23","author":"D Ma","year":"2022","unstructured":"Ma D, Fang H, Wang N, Zhang C, Dong J, Hu H (2022) Automatic detection and counting system for pavement cracks based on PCGAN and YOLO-MF. IEEE Trans Intell Transp Syst 23:22166\u201322178","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1130_CR46","doi-asserted-by":"publisher","unstructured":"Wang W, Wu B, Yang S, Wang Z (2018) Road damage detection and classification with faster R-CNN. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, pp. 5220\u20135223. https:\/\/doi.org\/10.1109\/BigData.2018.8622354","DOI":"10.1109\/BigData.2018.8622354"},{"key":"1130_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-024-06129-0","volume":"6","author":"Y Jiang","year":"2024","unstructured":"Jiang Y (2024) Road damage detection and classification using deep neural networks. Discov Appl Sci 6:421. https:\/\/doi.org\/10.1007\/s42452-024-06129-0","journal-title":"Discov Appl Sci"},{"issue":"4","key":"1130_CR48","first-page":"4567","volume":"11","author":"A Abdelwahed","year":"2024","unstructured":"Abdelwahed A, Hassan MS, Tariq R (2024) TinyML-based road damage detection using quantized MobileNetV3 for edge deployment. IEEE Internet Things J 11(4):4567\u20134579","journal-title":"IEEE Internet Things J"},{"key":"1130_CR49","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. [Online]. https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"1130_CR50","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. https:\/\/arxiv.org\/abs\/1512.03385"},{"key":"1130_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. https:\/\/arxiv.org\/abs\/1512.00567","DOI":"10.1109\/CVPR.2016.308"},{"key":"1130_CR52","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely connected convolutional networks. https:\/\/arxiv.org\/abs\/1608.06993"},{"key":"1130_CR53","unstructured":"Tan M, L QV (2020) EfficientNet: rethinking model scaling for convolutional neural networks, pp.\u00a011946. [Online]. https:\/\/arxiv.org\/abs\/1905.11946"},{"key":"1130_CR54","unstructured":"Dosovitskiy A et al (2010) An image is worth 16x16 words: transformers for image recognition at scale, pp. 11929. https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"1130_CR55","doi-asserted-by":"crossref","unstructured":"Liu Z et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In:\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10012\u201310022.\u00a0https:\/\/arxiv.org\/abs\/2103.14030","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1130_CR56","doi-asserted-by":"publisher","unstructured":"Jin X, Han J (2011) K-means clustering. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp. 564\u2013568. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_425","DOI":"10.1007\/978-0-387-30164-8_425"},{"key":"1130_CR57","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you? Explaining the predictions of any classifier. In:\u00a0Proceedings of the (2016) Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, pp. 97\u2013101. [Online]. https:\/\/aclanthology.org\/N16-3020\/","DOI":"10.18653\/v1\/N16-3020"},{"key":"1130_CR58","doi-asserted-by":"publisher","unstructured":"Byun H, Lee SW (2002) Applications of support vector machines for pattern recognition: a survey. In: :Lee S-W, Verri A (eds) Pattern recognition with support vector machines\u00a0. Springer, Berlin, pp. 213\u2013236. https:\/\/doi.org\/10.1007\/3-540-45665-1_17","DOI":"10.1007\/3-540-45665-1_17"},{"issue":"6","key":"1130_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3459665","volume":"54","author":"P Cunningham","year":"2021","unstructured":"Cunningham P, Delany SJ (2021) <article-title update=\"added\">k-Nearest neighbour classifiers - a tutorial. ACM Comput Surv 54(6):1\u201325. https:\/\/doi.org\/10.1145\/3459665","journal-title":"ACM Comput Surv"},{"issue":"1","key":"1130_CR60","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106. https:\/\/doi.org\/10.1007\/BF00116251","journal-title":"Mach Learn"},{"key":"1130_CR61","unstructured":"Vikramkumar VB, Vijaykumar VB, Trilochan T (2014) Bayes and naive bayes classifier. [Online]. https:\/\/arxiv.org\/abs\/1404.0933"},{"key":"1130_CR62","unstructured":"Chung MK (2020) Introduction to logistic regression. [Online]. Available: https:\/\/arxiv.org\/abs\/2008.13567"},{"key":"1130_CR63","unstructured":"Yu Y, Zhang Y (2021) Multi-layer perceptron trainability explained via variability. [Online]. Available: https:\/\/arxiv.org\/abs\/2105.08911"},{"key":"1130_CR64","doi-asserted-by":"publisher","unstructured":"Chikha WB, Chaoui S, Attia R (2017) Performance of AdaBoost classifier in recognition of superposed modulations for MIMO TWRC with physical-layer network coding. In:\u00a0Proc. 25th Int. Conf. Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, pp. 1\u20135. [Online]. https:\/\/doi.org\/10.23919\/SOFTCOM.2017.8115498","DOI":"10.23919\/SOFTCOM.2017.8115498"},{"key":"1130_CR65","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, pp. 785\u2013794. [Online]. Available: https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"1130_CR66","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach Learn"},{"issue":"1","key":"1130_CR67","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.dcan.2022.10.004","volume":"9","author":"D Mishra","year":"2023","unstructured":"Mishra D, Naik B, Nayak J, Souri A, Dash PB, Vimal S (2023) Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network. Digit Commun Networks 9(1):125\u2013137. https:\/\/doi.org\/10.1016\/j.dcan.2022.10.004","journal-title":"Digit Commun Networks"},{"key":"1130_CR68","doi-asserted-by":"publisher","unstructured":"Ding W, Huang C, Zhang Y, Li J, Zhao H (2022) An ensemble of one-stage and two-stage detectors approach for road damage detection. In: 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, pp. 6395\u20136400. https:\/\/doi.org\/10.1109\/BigData55660.2022.10021000","DOI":"10.1109\/BigData55660.2022.10021000"},{"key":"1130_CR69","doi-asserted-by":"crossref","unstructured":"Li Y et al (2024) RDD-YOLO: road damage detection algorithm based on improved YOLOv8. Appl Sci 14:8","DOI":"10.3390\/app14083360"},{"key":"1130_CR70","doi-asserted-by":"publisher","first-page":"3542","DOI":"10.1038\/s41598-022-07527-3","volume":"12","author":"K Guo","year":"2022","unstructured":"Guo K, He C, Yang M, Wang S (2022) A pavement distresses identification method optimized for YOLOv5s. Sci Rep 12:3542","journal-title":"Sci Rep"},{"key":"1130_CR71","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. https:\/\/arxiv.org\/abs\/1610.02357","DOI":"10.1109\/CVPR.2017.195"},{"key":"1130_CR72","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. https:\/\/arxiv.org\/abs\/1602.07261","DOI":"10.1609\/aaai.v31i1.11231"},{"issue":"8","key":"1130_CR73","doi-asserted-by":"publisher","DOI":"10.3390\/app14083360","volume":"14","author":"Y Li","year":"2024","unstructured":"Li Y, Yin C, Lei Y, Zhang J, Yan Y (2024) RDD-YOLO: Road damage detection algorithm based on improved You Only Look Once version 8. Appl Sci 14(8):3360. https:\/\/doi.org\/10.3390\/app14083360","journal-title":"Appl Sci"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01130-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-025-01130-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-025-01130-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:09:38Z","timestamp":1773655778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-025-01130-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,10]]},"references-count":75,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1130"],"URL":"https:\/\/doi.org\/10.1007\/s12065-025-01130-x","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,10]]},"assertion":[{"value":"14 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"20"}}