Stefenon, Stefano FrizzoSeman, Laio OrielKlaar, Anne Carolina RodriguesOvejero, Raúl GarcíaLeithardt, Valderi Reis Quietinho2024-09-302024-09-302024-062090-4479PURE: 100329988PURE UUID: 87086fff-d910-46f2-87bb-7c5ba9fc93bcScopus: 85188210094WOS: 001261986800001http://hdl.handle.net/10362/172770This work was supported by “Caracterización, análisis e intervención en la prevención de riesgos laborales en entornos de trabajo tradicionales mediante la aplicación de tecnologías disruptivas”. De la Consejeria de Empleo e Industria, under project 2022/00384/001. Publisher Copyright: © 2024 The Author(s)Ensuring the reliability of electrical distribution networks is a pressing concern, especially given the power outages due to surface contamination on insulating components. Surface contamination can elevate surface conductivity, thereby resulting in failures that can lead to power shutdowns. Addressing this challenge, this paper proposes an approach for real-time monitoring of electrical distribution grids to prevent such incidents. A hypertuned version of the you only look once (YOLO) model is tailored for this application. We refine the model's hyperparameters by integrating a genetic algorithm to maximize its detection performance. The EigenCAM technique enhances the visual interpretability of the model's outcomes, providing operators with actionable insights for maintenance and monitoring tasks. Benchmark tests reveal that the proposed Hypertuned-YOLO outperforms Detectron (Masked R-CNN), YOLOv5, and YOLOv7 models. The Hypertuned-YOLO achieves an F1-score of 0.867 and a mAP@0.5 of 0.922, validating its robustness and efficacy.118764732engConvolutional neural networksEigenCAMPower gridsYou only look onceGeneral Engineeringjournal article10.1016/j.asej.2024.102722https://www.scopus.com/pages/publications/85188210094