A carregar...
Publicação
Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 6.6 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Deep 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.
Descrição
This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant DDG-2024-00035, and in part by the Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG) under Grant DDG-2024-00035. Publisher Copyright: © The Author(s) 2026.
Palavras-chave
Bayesian optimization Diffusion models Explainable artificial intelligence Generative artificial intelligence Computer Science (miscellaneous) Electrical and Electronic Engineering