{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T21:37:32Z","timestamp":1774820252081,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00066\/2020"],"award-info":[{"award-number":["UIDB\/00066\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber\u2013Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.<\/jats:p>","DOI":"10.3390\/app11073086","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:13:10Z","timestamp":1617149590000},"page":"3086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3777-1346","authenticated-orcid":false,"given":"Ricardo Silva","family":"Peres","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"UNINOVA-Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"}]},{"given":"Miguel","family":"Azevedo","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-8409","authenticated-orcid":false,"given":"Sara Oleiro","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"UNINOVA-Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-8349","authenticated-orcid":false,"given":"Magno","family":"Guedes","sequence":"additional","affiliation":[{"name":"Introsys S.A., Estrada dos 4 Castelos 67, 2950-805 Quinta do Anjo, Portugal"}]},{"given":"F\u00e1bio","family":"Miranda","sequence":"additional","affiliation":[{"name":"Introsys S.A., Estrada dos 4 Castelos 67, 2950-805 Quinta do Anjo, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6348-1847","authenticated-orcid":false,"given":"Jos\u00e9","family":"Barata","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"UNINOVA-Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","article-title":"Segmentation-based deep-learning approach for surface-defect detection","volume":"31","author":"Tabernik","year":"2020","journal-title":"J. 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