{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T20:31:57Z","timestamp":1782765117619,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T00:00:00Z","timestamp":1623369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.<\/jats:p>","DOI":"10.3390\/s21124026","type":"journal-article","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T02:55:28Z","timestamp":1623380128000},"page":"4026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Aircraft Fuselage Corrosion Detection Using Artificial Intelligence"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6167-8104","authenticated-orcid":false,"given":"Bruno","family":"Brandoli","sequence":"first","affiliation":[{"name":"Department of Computer Science, Institute for Big Data Analytics, Dalhousie University, Halifax, NS B3H 1W5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2017-9277","authenticated-orcid":false,"given":"Andr\u00e9 R.","family":"de Geus","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6422-4722","authenticated-orcid":false,"given":"Jefferson R.","family":"Souza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Uberlandia, Uberlandia 38400-902, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8437-4349","authenticated-orcid":false,"given":"Gabriel","family":"Spadon","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-3805","authenticated-orcid":false,"given":"Amilcar","family":"Soares","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8318-1780","authenticated-orcid":false,"suffix":"Jr.","given":"Jose F.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerzy","family":"Komorowski","sequence":"additional","affiliation":[{"name":"JPWK Aerospace, Ontario, ON K1A 0R6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6629-8434","authenticated-orcid":false,"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute for Big Data Analytics, Dalhousie University, Halifax, NS B3H 1W5, Canada"},{"name":"Institute for Computer Science, Polish Academy of Sciences, 01-248 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106420","DOI":"10.1016\/j.oceaneng.2019.106420","article-title":"On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances","volume":"190","author":"Ortiz","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102134","DOI":"10.1016\/j.ndteint.2019.102134","article-title":"Visual inspection and characterization of external corrosion in pipelines using deep neural network","volume":"107","author":"Bastian","year":"2019","journal-title":"NDT Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.conbuildmat.2019.07.293","article-title":"Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer","volume":"226","author":"Ali","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.engstruct.2019.02.018","article-title":"Reliability-based life-cycle-cost comparison of different corrosion management strategies","volume":"186","author":"Sajedi","year":"2019","journal-title":"Eng. Struct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11931","DOI":"10.1039\/D0CC03061A","article-title":"Fighting corrosion with stimuli-responsive polymer conjugates","volume":"56","author":"Seidi","year":"2020","journal-title":"Chem. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.corsci.2010.11.005","article-title":"The study of intergranular corrosion in aircraft aluminium alloys using X-ray tomography","volume":"53","author":"Knight","year":"2011","journal-title":"Corros. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"061004","DOI":"10.1063\/2.1106104","article-title":"Influence of environmental factors on corrosion damage of aircraft structure","volume":"1","author":"Ren","year":"2011","journal-title":"Theor. Appl. Mech. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.ijfatigue.2003.08.005","article-title":"Modeling the effects of prior exfoliation corrosion on fatigue life of aircraft wing skins","volume":"25","author":"Liao","year":"2003","journal-title":"Int. J. Fatigue"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, S., He, Y., Zhang, T., Wang, G., and Du, X. (2018). Long-Term Atmospheric Corrosion Behavior of Epoxy Prime Coated Aluminum Alloy 7075-T6 in Coastal Environment. Materials, 11.","DOI":"10.3390\/ma11060965"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TIM.2007.908139","article-title":"Survey: State of the Art in NDE Data Fusion Techniques","volume":"56","author":"Liu","year":"2007","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103047","DOI":"10.1016\/j.infrared.2019.103047","article-title":"Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing","volume":"102","author":"Chulkov","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.ijfatigue.2006.07.003","article-title":"Fatigue modeling for aircraft structures containing natural exfoliation corrosion","volume":"29","author":"Liao","year":"2007","journal-title":"Int. J. Fatigue"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, W., Wang, X., and Sun, B. (2016). Lamb-Wave-Based Tomographic Imaging Techniques for Hole-Edge Corrosion Monitoring in Plate Structures. Materials, 9.","DOI":"10.3390\/ma9110916"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, H., Yi, J., Xu, Y., Wang, Y., and Qing, X. (2019). Identification and Compensation Technique of Non-Uniform Temperature Field for Lamb Wave-and Multiple Sensors-Based Damage Detection. Sensors, 19.","DOI":"10.3390\/s19132930"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105988","DOI":"10.1016\/j.ultras.2019.105988","article-title":"Corrosion monitoring using a new compressed sensing-based tomographic method","volume":"101","author":"Chang","year":"2020","journal-title":"Ultrasonics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Towsyfyan, H., Biguri, A., Boardman, R., and Blumensath, T. (2019). Successes and challenges in non-destructive testing of aircraft composite structures. Chin. J. Aeronaut.","DOI":"10.1016\/j.cja.2019.09.017"},{"key":"ref_17","first-page":"775","article-title":"Neural Network Based Processing of Thermal NDE Data for Corrosion Detection","volume":"Volume 12","author":"Thompson","year":"1993","journal-title":"Review of Progress in Quantitative Nondestructive Evaluation"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.2514\/2.94","article-title":"Corrosion pillowing stresses in fuselage lap joints","volume":"35","author":"Bellinger","year":"1997","journal-title":"AIAA J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/S1000-9361(11)60254-9","article-title":"Corrosion Fatigue Life Prediction of Aircraft Structure Based on Fuzzy Reliability Approach","volume":"18","author":"Tan","year":"2005","journal-title":"Chin. J. Aeronaut."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hoeppner, D.W., and Arriscorreta, C.A. (2012). Exfoliation Corrosion and Pitting Corrosion and Their Role in Fatigue Predictive Modeling: State-of-the-Art Review. Int. J. Aerosp. Eng., 2012.","DOI":"10.1155\/2012\/191879"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gialanella, S., and Malandruccolo, A. (2020). Corrosion. Aerospace Alloys, Springer International Publishing.","DOI":"10.1007\/978-3-030-24440-8"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105426","DOI":"10.1016\/j.ijfatigue.2019.105426","article-title":"Fatigue crack growth lessons from thirty-five years of the Royal Australian Air Force F\/A-18 A\/B Hornet Aircraft Structural Integrity Program","volume":"133","author":"Main","year":"2020","journal-title":"Int. J. Fatigue"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.proeng.2017.04.497","article-title":"Monitoring Aircraft Microclimate and Corrosion","volume":"188","author":"Ganther","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, L., Chakik, M., and Prakash, R. (2021). A Review of Corrosion in Aircraft Structures and Graphene-Based Sensors for Advanced Corrosion Monitoring. Sensors, 21.","DOI":"10.3390\/s21092908"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Phan, H., Nguyen, H.L., Ch\u00e9n, O.Y., Pham, L.D., Koch, P., McLoughlin, I.V., and Mertins, A. (2021, January 6\u201311). Multi-view Audio and Music Classification. Proceedings of the ICASSP 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414551"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Spadon, G., Hong, S., Brandoli, B., Matwin, S., Rodrigues, J.F., and Sun, J. (2021). Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3076155"},{"key":"ref_27","unstructured":"Gupta, T., Kamath, A., Kembhavi, A., and Hoiem, D. (2021). Towards General Purpose Vision Systems. arXiv."},{"key":"ref_28","unstructured":"Malekzadeh, T., Abdollahzadeh, M., Nejati, H., and Cheung, N. (2017). Aircraft Fuselage Defect Detection using Deep Neural Networks. arXiv."},{"key":"ref_29","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_31","first-page":"49","article-title":"Machine learning approaches for defect classification on aircraft fuselage images aquired by an UAV","volume":"Volume 11172","author":"Cudel","year":"2019","journal-title":"Proceedings of the Fourteenth International Conference on Quality Control by Artificial Vision"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.sysarc.2013.12.003","article-title":"CaRINA Intelligent Robotic Car: Architectural design and applications","volume":"60","author":"Fernandes","year":"2014","journal-title":"J. Syst. Archit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.ins.2020.09.024","article-title":"LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks","volume":"545","author":"Rodrigues","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dos Santos de Arruda, M., Spadon, G., Rodrigues, J.F., Gon\u00e7alves, W.N., and Brandoli, B. (2018, January 8\u201313). Recognition of Endangered Pantanal Animal Species using Deep Learning Methods. Proceedings of the 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489369"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1109\/LGRS.2017.2743715","article-title":"Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images","volume":"14","author":"Tetila","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 13\u201316). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41551-019-0487-z","article-title":"Detection of anaemia from retinal fundus images via deep learning","volume":"4","author":"Mitani","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_38","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <1 MB model size. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_41","unstructured":"Flennerhag, S., Rusu, A.A., Pascanu, R., Visin, F., Yin, H., and Hadsell, R. (2020, January 26\u201330). Meta-Learning with Warped Gradient Descent. Proceedings of the 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia. Available online: OpenReview.net."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Abati, D., Tomczak, J., Blankevoort, T., Calderara, S., Cucchiara, R., and Bejnordi, B.E. (2020, January 13\u201319). Conditional Channel Gated Networks for Task-Aware Continual Learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00399"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jiang, X., Ding, L., Havaei, M., Jesson, A., and Matwin, S. (2019). Task Adaptive Metric Space for Medium-Shot Medical Image Classification. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019, Springer International Publishing.","DOI":"10.1007\/978-3-030-32239-7_17"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 1\u201326). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning Transferable Architectures for Scalable Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13590","DOI":"10.1038\/s41598-020-70479-z","article-title":"Comparing different deep learning architectures for classification of chest radiographs","volume":"10","author":"Bressem","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., and Le, Q.V. (2020, January 14\u201319). Self-Training With Noisy Student Improves ImageNet Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020). Big Transfer (BiT): General Visual Representation Learning. Computer Vision\u2014ECCV 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-58592-1"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Abhishek Das, R.V., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4026\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:10Z","timestamp":1760163190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,11]]},"references-count":50,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21124026"],"URL":"https:\/\/doi.org\/10.3390\/s21124026","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,11]]}}}