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Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection

2026Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorMatos-Carvalho, João Pedro
dc.contributor.authorMariani, Viviana Cocco
dc.contributor.authorCoelho, Leandro dos Santos
dc.contributor.authorYow, Kin-Choong
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.editorSpringer Nature
dc.date.accessioned2026-09-25T10:48:58Z
dc.date.available2026-09-25T10:48:58Z
dc.date.issued2026
dc.description.abstractDeep learning-based autonomous inspection of power grid insulators is challenged by data imbalance and model opacity. This paper presents an end-to-end solution integrating advanced data synthesis, detection, classification, and explainability. First, a conditional diffusion model generates realistic synthetic fault images to balance the dataset. A two-stage architecture based on You Only Look Once version 26 (YOLO26) extra-large and Shifted windows (Swin)-V2-B, called YOLO26-Swin, fine-tuned with Bayesian optimization, performs robust insulator detection and then fault classification. Finally, a novel SHapley Additive exPlanations with Class Activation Mapping (SHAP-CAM) method provides intuitive visual explanations for model predictions. Extensive experiments validate our framework’s superiority: it achieves an F1-score of 0.98149 and a mean Average Precision (mAP)@[0.5] of 0.98951, exceeding leading detection and classification models. This work highlights the efficacy of diffusion models for data augmentation in critical infrastructure and advances the interpretability of vision-based inspection systems.eng
dc.identifier.citationStefenon, S. F., Matos-Carvalho, J. P., Mariani, V. C., et al. (2026). Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection. Autonomous Intelligent Systems, 6, 13. https://doi.org/10.1007/s43684-026-00135-2
dc.identifier.doi10.1007/s43684-026-00135-2
dc.identifier.issn2730-616X
dc.identifier.urihttp://hdl.handle.net/10400.21/23201
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationUID/00408/2025; UID/PRR/00408/2025; 2023.15441.TENURE.051/CP00003/CT00029
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s43684-026-00135-2
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian optimization
dc.subjectDiffusion models
dc.subjectGenerative artificial intelligence
dc.subjectExplainable artificial intelligence
dc.titleDiffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspectioneng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue13
oaire.citation.titleAutonomous Intelligent Systems
oaire.citation.volume6
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameInstituto Politécnico de Lisboa, Instituto Superior de Engenharia de Lisboa
person.familyNameStefenon
person.givenNameStefano Frizzo
person.identifier.orcidhttps://orcid.org/0000-0002-3723-616X
relation.isAuthorOfPublicationb0383f14-261a-42cf-a223-7c571c84a241
relation.isAuthorOfPublication.latestForDiscoveryb0383f14-261a-42cf-a223-7c571c84a241

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