{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:19:05Z","timestamp":1783527545679,"version":"3.55.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"vor","delay-in-days":51,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005729","name":"University of Salento","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005729","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.02.200","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T07:17:59Z","timestamp":1774250279000},"page":"1621-1630","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Benchmarking AI-based visual inspection systems for aerospace quality control: A multi-vendor comparative evaluation"],"prefix":"10.1016","volume":"277","author":[{"given":"Angelo","family":"Corallo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vito Del","family":"Vecchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Di Prizio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Buscicchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.procs.2026.02.200_bib1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1002\/ags3.12513","article-title":"\u00abArtificial intelligence-based computer vision in surgery: Recent advances and future perspectives\u00bb","volume":"6","author":"Kitaguchi","year":"2022","journal-title":"Annals of Gastroenterological Surgery"},{"key":"10.1016\/j.procs.2026.02.200_bib2","doi-asserted-by":"crossref","unstructured":"J. Aust, S. Shankland, D. Pons, R. Mukundan, e A. Mitrovic, \u00abAutomated Defect Detection and Decision-Support in Gas Turbine Blade Inspection\u00bb, Aerospace, vol. 8, fasc. 2, Art. fasc. 2, feb. 2021, doi: 10.3390\/aerospace8020030.","DOI":"10.3390\/aerospace8020030"},{"key":"10.1016\/j.procs.2026.02.200_bib3","doi-asserted-by":"crossref","unstructured":"B. Brandoli et al., \u00abAircraft Fuselage Corrosion Detection Using Artificial Intelligence\u00bb, Sensors, vol. 21, fasc. 12, Art. fasc. 12, gen. 2021, doi: 10.3390\/s21124026.","DOI":"10.3390\/s21124026"},{"key":"10.1016\/j.procs.2026.02.200_bib4","doi-asserted-by":"crossref","unstructured":"A. Corallo, V. Del Vecchio, e A. Di Prizio, Advanced AI-Based Solutions for Visual Inspection: A Systematic Literature Review. 2024, p. 664. doi: 10.5220\/0012618000003690.","DOI":"10.5220\/0012618000003690"},{"key":"10.1016\/j.procs.2026.02.200_bib5","doi-asserted-by":"crossref","unstructured":"S. A. Cottrell, An Introduction to Metallurgy, Second Edition, 2a ed. London: CRC Press, 2019. doi: 10.1201\/9780429293917.","DOI":"10.1201\/9780429293917"},{"key":"10.1016\/j.procs.2026.02.200_bib6","unstructured":"American Bureau of Shipping, \u00abThe use of remote inspection technologies\u00bb. Consultato: 30 dicembre 2023. [Online]. Disponibile su: https:\/\/ww2.eagle.org\/content\/dam\/eagle\/rules-and-guides\/current\/other\/242-gn-remote-inspection-tech-dec-2022\/rit-gn-dec22.pdf"},{"key":"10.1016\/j.procs.2026.02.200_bib7","unstructured":"J. Yang, R. Xu, Z. Qi, e Y. Shi, \u00abVisual Anomaly Detection for Images: A Survey\u00bb, 27 settembre 2021, arXiv: arXiv:2109.13157. doi: 10.48550\/arXiv.2109.13157."},{"key":"10.1016\/j.procs.2026.02.200_bib8","doi-asserted-by":"crossref","unstructured":"C. El Zant, Q. Charrier, K. Benfriha, e P. Le Men, \u00abEnhanced Manufacturing Execution System \u201cMES\u201d Through a Smart Vision System\u00bb, in Advances on Mechanics, Design Engineering and Manufacturing III, L. Roucoules, M. Paredes, B. Eynard, P. Morer Camo, e C. Rizzi, A c. di, in Lecture Notes in Mechanical Engineering. Cham: Springer International Publishing, 2021, pp. 329\u2013334. doi: 10.1007\/978-3-030-70566-4_52.","DOI":"10.1007\/978-3-030-70566-4_52"},{"key":"10.1016\/j.procs.2026.02.200_bib9","unstructured":"J. Brownlee, \u00abDeep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python\u00bb. Consultato: 11 dicembre 2023. [Online]. Disponibile su: https:\/\/b.eruditor.link\/file\/3706194\/"},{"key":"10.1016\/j.procs.2026.02.200_bib10","doi-asserted-by":"crossref","unstructured":"V. Piuri, F. Scotti, e M. Roveri, \u00abComputational intelligence in industrial quality control\u00bb, in IEEE International Workshop on Intelligent Signal Processing, 2005., set. 2005, pp. 4\u20139. doi: 10.1109\/WISP.2005.1531623.","DOI":"10.1109\/WISP.2005.1531623"},{"key":"10.1016\/j.procs.2026.02.200_bib11","unstructured":"A. Ricci e D. Surpi, \u00abL\u2019ACCIAIO E I SUOI DIFETTI\u00bb, TRAFILIX. Consultato: 27 giugno 2025. [Online]. Disponibile su: https:\/\/lucefin.com\/pubblicazioni-lacciaio-e-i-suoi-difetti\/"},{"key":"10.1016\/j.procs.2026.02.200_bib12","doi-asserted-by":"crossref","unstructured":"J. W. Bennet, B. C. Mecrow, D. J. Atkinson, e G. J. Atkinson, \u00abSafety-critical design of electromechanical actuation systems in commercial aircraft | Request PDF\u00bb, ResearchGate, 2011, doi: 10.1049\/iet-epa.2009.0304.","DOI":"10.1049\/iet-epa.2009.0304"},{"key":"10.1016\/j.procs.2026.02.200_bib13","unstructured":"N. Vishnu, \u00abAnomaly Detection and Data Science | A Key to Manufacturing Excellence\u00bb. Consultato: 27 giugno 2025. [Online]. Disponibile su: https:\/\/thinkpalm.com\/blogs\/anomaly-detection-data-science-a-key-to-manufacturing-excellence\/"},{"key":"10.1016\/j.procs.2026.02.200_bib14","doi-asserted-by":"crossref","unstructured":"N. H\u00fctten, F. H\u00f6lken, M. A. Gomes, e K. Andricevic, \u00ab(PDF) Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers\u00bb, ResearchGate, 2024, doi: 10.3390\/asi7010011.","DOI":"10.3390\/asi7010011"},{"key":"10.1016\/j.procs.2026.02.200_bib15","unstructured":"R. Slisarenko, \u00abHow Visual AI Minimizes Defects and Boosts Yield in Manufacturing\u00bb, Blog. Consultato: 27 giugno 2025. [Online]. Disponibile su: https:\/\/tech-stack.com\/blog\/visual-ai-reduces-defects-boosts-manufacturing-yield\/"},{"key":"10.1016\/j.procs.2026.02.200_bib16","doi-asserted-by":"crossref","unstructured":"P. Trampert, S. Mantowsky, F. Schmidt, e T. Masiak, \u00ab(PDF) AI-Driven Toolbox for Efficient and Transferable Visual Quality Inspection in Production\u00bb, ResearchGate, giu. 2025, doi: 10.1007\/s42979-025-03988-1.","DOI":"10.1007\/s42979-025-03988-1"},{"key":"10.1016\/j.procs.2026.02.200_bib17","unstructured":"J. P. Mueller e L. Massaron, Machine learning for dummies, 2nd edition. in For dummies. Hoboken, NJ: John Wiley & Sons, Inc, 2021."},{"key":"10.1016\/j.procs.2026.02.200_bib18","doi-asserted-by":"crossref","first-page":"e7068349","DOI":"10.1155\/2018\/7068349","article-title":"\u00abDeep Learning for Computer Vision: A Brief Review\u00bb","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.1016\/j.procs.2026.02.200_bib19","unstructured":"S. Maschione, \u00abCos\u2019\u00e8 il Supervised Machine Learning\u00bb, IT PARTNER ITALIA. Consultato: 25 giugno 2025. [Online]. Disponibile su: https:\/\/www.itpartneritalia.com\/cose-il-supervised-machine-learning\/"},{"key":"10.1016\/j.procs.2026.02.200_bib20","unstructured":"D. Bergmann e C. Stryker, \u00abCos\u2019\u00e8 l\u2019addestramento del modello? | IBM\u00bb. Consultato: 25 giugno 2025. [Online]. Disponibile su: https:\/\/www.ibm.com\/it-it\/think\/topics\/model-training"},{"key":"10.1016\/j.procs.2026.02.200_bib21","unstructured":"M. Manfredi, \u00abModelli di machine learning: cosa sono e come scegliere il migliore\u00bb, BNova. Consultato: 25 giugno 2025. [Online]. Disponibile su: https:\/\/www.bnova.it\/data-science\/machine-learning-modelli\/"},{"key":"10.1016\/j.procs.2026.02.200_bib22","doi-asserted-by":"crossref","first-page":"100933","DOI":"10.1016\/j.aei.2019.100933","article-title":"\u00abOne class based feature learning approach for defect detection using deep autoencoders\u00bb","volume":"42","author":"Mujeeb","year":"2019","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.procs.2026.02.200_bib23","doi-asserted-by":"crossref","unstructured":"S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, e B. Lee, \u00abA Survey of Modern Deep Learning based Object Detection Models\u00bb, 12 maggio 2021, arXiv: arXiv:2104.11892. doi: 10.48550\/arXiv.2104.11892.","DOI":"10.1016\/j.dsp.2022.103514"},{"key":"10.1016\/j.procs.2026.02.200_bib24","unstructured":"A. Esposito, \u00abclassificazione e rilevamento del danno in sistemi di monitoraggio strutturale mediante tecniche di machine learning\u00bb, Tesi di laurea, 2024. Consultato: 25 giugno 2025. [Online]. Disponibile su: https:\/\/amslaurea.unibo.it\/id\/eprint\/30480\/"},{"key":"10.1016\/j.procs.2026.02.200_bib25","unstructured":"K. Wiegers e J. Beatty, Software Requirements. Pearson Education, 2013."},{"key":"10.1016\/j.procs.2026.02.200_bib26","doi-asserted-by":"crossref","unstructured":"P. Becker, G. Tebes, D. Peppino, e L. A. Olsina Santos, \u00abApplying an Improving Strategy that embeds Functional and Non-Functional Requirements Concepts\u00bb, Aplicando una estrategia de mejora que incluye conceptos de requisitos funcionales y no funcionales, vol. 19, fasc. 2, ott. 2019, Consultato: 25 giugno 2025. [Online]. Disponibile su: http:\/\/sedici.unlp.edu.ar\/handle\/10915\/87772","DOI":"10.24215\/16666038.19.e15"},{"issue":"1","key":"10.1016\/j.procs.2026.02.200_bib27","first-page":"737626","article-title":"\u00abAn Approach for Integrating the Prioritization of Functional and Nonfunctional Requirements\u00bb","volume":"2014","author":"Dabbagh","year":"2014","journal-title":"The Scientific World Journal"},{"key":"10.1016\/j.procs.2026.02.200_bib28","unstructured":"R. S. Pressman, Software engineering: a practitioner\u2019s approach, 5. ed., 20. anniversary ed. in McGraw-Hill series in computer science software engineering and databases. Boston, Mass: McGraw Hill, 2001."},{"key":"10.1016\/j.procs.2026.02.200_bib29","unstructured":"I. Salinari, \u00abLinee guida per la gestione dei requisiti\u00bb, 2014. Consultato: 27 febbraio 2023. [Online]. Disponibile su: https:\/\/www.slideshare.net\/tecnetdati\/gestione-dei-requisiti"},{"key":"10.1016\/j.procs.2026.02.200_bib30","unstructured":"I. Sommerville e G. Kotonya, \u00abRequirements Engineering: Processes and Techniques | Wiley\u00bb, Wiley.com. Consultato: 27 febbraio 2023. [Online]. Disponibile su: https:\/\/www.wiley.com\/en-us\/Requirements+Engineering%3A+Processes+and+Techniques-p-9780471972082"},{"key":"10.1016\/j.procs.2026.02.200_bib31","unstructured":"I. Sommerville e P. Sawyer, \u00abRequirements engineering: A good practical guide | Lancaster University\u00bb. Consultato: 27 febbraio 2023. [Online]. Disponibile su: https:\/\/www.research.lancs.ac.uk\/portal\/en\/publications\/requirements-engineering(5637f4c4-7c40-4043-90db-49447088d947).html"},{"key":"10.1016\/j.procs.2026.02.200_bib32","unstructured":"IEEE Standards Association;, \u00abIeee 830\u00bb, IEEE Standards Association. Consultato: 28 maggio 2025. [Online]. Disponibile su: https:\/\/standards.ieee.org\/ieee\/830\/1222\/"},{"key":"10.1016\/j.procs.2026.02.200_bib33","unstructured":"G. Booch, J. Rumbaugh, e I. Jacobson, The unified modeling language user guide. in The Addison-Wesley object technology series. Reading (Mass.): Addison-Wesley, 1999."},{"key":"10.1016\/j.procs.2026.02.200_bib34","doi-asserted-by":"crossref","unstructured":"X. Li, Z. Liu, e H. Jifeng, \u00abA formal semantics of UML sequence diagram\u00bb, in 2004 Australian Software Engineering Conference. Proceedings., apr. 2004, pp. 168\u2013177. doi: 10.1109\/ASWEC.2004.1290469.","DOI":"10.1109\/ASWEC.2004.1290469"},{"key":"10.1016\/j.procs.2026.02.200_bib35","unstructured":"G. Booch, J. Rumbaugh, e I. Jacobson, The unified modeling language user guide. in The Addison-Wesley object technology series. Reading (Mass.): Addison-Wesley, 1999."},{"key":"10.1016\/j.procs.2026.02.200_bib36","unstructured":"M. Lucarelli, \u00abUnified Modeling Language\u00bb. Consultato: 25 giugno 2025. [Online]. Disponibile su: http:\/\/matteolucarelli.altervista.org\/uml\/uml.htm"},{"key":"10.1016\/j.procs.2026.02.200_bib37","unstructured":"A. Sinibaldi, \u00abSviluppo sicuro del software, come formalizzarlo: ingegneria dei requisiti\u00bb, Cyber Security 360. Consultato: 25 giugno 2025. [Online]. Disponibile su: https:\/\/www.cybersecurity360.it\/soluzioni-aziendali\/ingegneria-dei-requisiti-ecco-come-formalizzare-lo-sviluppo-sicuro-del-software\/"},{"key":"10.1016\/j.procs.2026.02.200_bib38","first-page":"81","article-title":"\u00abUsing competitive benchmarking to set goals\u00bb","volume":"25","author":"Vaziri","year":"1992","journal-title":"Quality Progress"},{"key":"10.1016\/j.procs.2026.02.200_bib39","unstructured":"R. C. Camp, Benchmarking: the search for industry best practices that lead to superior performance. Asq Press, 1989."},{"key":"10.1016\/j.procs.2026.02.200_bib40","first-page":"27","article-title":"\u00abEvaluation measures for models assessment over imbalanced data sets\u00bb","volume":"3","author":"Bekkar","year":"2013","journal-title":"Journal of Information Engineering and Applications"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926003182?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926003182?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:34:24Z","timestamp":1783524864000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926003182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":40,"alternative-id":["S1877050926003182"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.02.200","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Benchmarking AI-based visual inspection systems for aerospace quality control: A multi-vendor comparative evaluation","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.02.200","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}