{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:31:55Z","timestamp":1772044315018,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03856-y","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:32:03Z","timestamp":1743222723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions"],"prefix":"10.1007","volume":"6","author":[{"given":"Shailaja","family":"Pasupuleti","sequence":"first","affiliation":[]},{"given":"K.","family":"Ramalakshmi","sequence":"additional","affiliation":[]},{"given":"Hemalatha","family":"Gunasekaran","sequence":"additional","affiliation":[]},{"given":"Rex Macedo","family":"Arokiaraj","sequence":"additional","affiliation":[]},{"given":"Saswati","family":"Debnath","sequence":"additional","affiliation":[]},{"given":"T. Jemima","family":"Jebaseeli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"key":"3856_CR1","doi-asserted-by":"crossref","unstructured":"Bouarfa S, Do\u011fru A, Arizar R, Aydo\u011fan R, Serafico J. Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask R-CNN. AIAA Scitech 2020 forum, 6\u201310 January 2020, Orlando, FL.","DOI":"10.2514\/6.2020-0389"},{"key":"3856_CR2","unstructured":"Drone-based aircraft damage inspection system. https:\/\/www.mrodrone.net\/. Accessed 17 June 2024."},{"issue":"6","key":"3856_CR3","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.24.6.061110","volume":"24","author":"I Jovancevic","year":"2015","unstructured":"Jovancevic I, Larnier S, Orteu JJ, Sentenac T. Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot. J Electron Imaging Soc Photo-Opt Instrum Eng. 2015;24(6): 061110.","journal-title":"J Electron Imaging Soc Photo-Opt Instrum Eng"},{"key":"3856_CR4","unstructured":"Brownlee J. A gentle introduction to object recognition with deep learning. Deep learning for computer vision. 2019."},{"key":"3856_CR5","doi-asserted-by":"publisher","first-page":"109914","DOI":"10.1016\/j.knosys.2022.109914","volume":"257","author":"R Romero","year":"2022","unstructured":"Romero R, Celard P, Sorribes-Fdez JM, SearaVieira A, Iglesias EL, Borrajo L. MobyDeep: a lightweight CNN architecture to configure models for text classification. Knowl Based Syst. 2022;257:109914.","journal-title":"Knowl Based Syst"},{"issue":"1","key":"3856_CR6","doi-asserted-by":"publisher","first-page":"252","DOI":"10.23919\/JSEE.2021.000022","volume":"32","author":"C Jing","year":"2021","unstructured":"Jing C, Dingqiang D. Inspection interval optimization for aircraft composite structures with dent and delamination damage. J Syst Eng Electron. 2021;32(1):252\u201360.","journal-title":"J Syst Eng Electron"},{"key":"3856_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"3856_CR8","first-page":"1","volume":"1574297","author":"Y Shin","year":"2019","unstructured":"Shin Y. Application of stochastic gradient boosting approach to early prediction of safety accidents at construction site. Adv Civ Eng. 2019;1574297:1\u20139.","journal-title":"Adv Civ Eng"},{"issue":"2","key":"3856_CR9","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TVCG.2020.3030352","volume":"27","author":"A Chatzimparmpas","year":"2020","unstructured":"Chatzimparmpas A, Martins RM, Kucher K, Kerren A. StackGenVis: alignment of data, algorithms, and models for stacking ensemble learning using performance metrics. IEEE Trans Vis Comput Graph. 2020;27(2):1547\u201357.","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"3856_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/aerospace7120171","volume":"7","author":"A Dogru","year":"2020","unstructured":"Dogru A, Bouarfa S, Arizar R, Aydogan R. Using convolutional neural networks to automate aircraft maintenance visual inspection. Aerospace. 2020;7:1\u201322.","journal-title":"Aerospace"},{"key":"3856_CR11","first-page":"1","volume":"70","author":"Q Xie","year":"2021","unstructured":"Xie Q, Lu D, Huang A, Yang J, Li D, Zhang Y, Wang J. RRCNet: rivet region classification network for rivet flush measurement based on 3-D point cloud. IEEE Trans Instrum Meas. 2021;70:1\u201312.","journal-title":"IEEE Trans Instrum Meas"},{"key":"3856_CR12","doi-asserted-by":"publisher","first-page":"4682","DOI":"10.3390\/s22134682","volume":"22","author":"NP Avdelidis","year":"2022","unstructured":"Avdelidis NP, Tsourdos A, Lafiosca P, Plaster R, Plaster A, Droznika M. Defects recognition algorithm development from visual UAV inspections. Sensors. 2022;22:4682.","journal-title":"Sensors"},{"issue":"3","key":"3856_CR13","first-page":"1","volume":"9","author":"Z Ameli","year":"2024","unstructured":"Ameli Z, Nesheli SJ, Landis EN. Deep learning-based steel bridge corrosion segmentation and condition rating using mask RCNN and YOLOv8. Infrastructures. 2024;9(3):1\u201316.","journal-title":"Infrastructures"},{"key":"3856_CR14","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.knosys.2012.08.024","volume":"37","author":"Q Da","year":"2013","unstructured":"Da Q. A competitive ensemble pruning approach based on cross-validation technique. Knowl Based Syst. 2013;37:394\u2013414.","journal-title":"Knowl Based Syst"},{"issue":"8","key":"3856_CR15","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/electronics8080832","volume":"8","author":"DV Carvalho","year":"2019","unstructured":"Carvalho DV, Pereira EM, Cardoso JS. Machine learning interpretability: a survey on methods and metrics. Electronics. 2019;8(8):832.","journal-title":"Electronics"},{"key":"3856_CR16","doi-asserted-by":"publisher","first-page":"3417","DOI":"10.3390\/s22093417","volume":"22","author":"OS Amosov","year":"2022","unstructured":"Amosov OS, Amosova SG, Iochkov IO. Deep neural network recognition of rivet joint defects in aircraft products. Sensors. 2022;22:3417.","journal-title":"Sensors"},{"key":"3856_CR17","doi-asserted-by":"publisher","first-page":"4026","DOI":"10.3390\/s21124026","volume":"21","author":"B Brandoli","year":"2021","unstructured":"Brandoli B, de Geus AR, Souza JR, Spadon G, Soares A, Rodrigues JF Jr, Komorowski J, Matwin S. Aircraft fuselage corrosion detection using artificial intelligence. Sensors. 2021;21:4026.","journal-title":"Sensors"},{"key":"3856_CR18","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"YJ Cha","year":"2017","unstructured":"Cha YJ, Choi W, B\u00fcy\u00fck\u00f6zt\u00fcrk O. Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng. 2017;32:361\u201378.","journal-title":"Comput Aided Civ Infrastruct Eng"},{"key":"3856_CR19","unstructured":"Fan Y, Deng Y, Zeng Z, Udpa L, Shih W, Fitzpatrick G. Aging aircraft rivet site inspection using magneto-optic imaging: automation and real-time image processing. In: Proceedings of the 9th joint FAA\/DoD\/NASA aging aircraft conference, Torrance, CA, USA, 6\u20139 March 2006."},{"issue":"3","key":"3856_CR20","first-page":"1","volume":"14","author":"R-C Hwang","year":"2014","unstructured":"Hwang R-C, Chen Y-J, Chang C-Y, Weng P-H, Wang H-L. An intelligent quality inspection system for the riveting process. Int J Eng Innov Res. 2014;14(3):1\u201317.","journal-title":"Int J Eng Innov Res"},{"key":"3856_CR21","unstructured":"https:\/\/www.vde-verlag.de\/proceedings-en\/565727090.html."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03856-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03856-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03856-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:32:20Z","timestamp":1743222740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03856-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,26]]},"references-count":21,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["3856"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03856-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,26]]},"assertion":[{"value":"22 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2025","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 do not have any competing interests and funding.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}}],"article-number":"321"}}