Enhanced High-Voltage Power Line Insulator and Contamination Classification using Score-Level Fusion
Abstract
High-voltage (HV) power line insulators are critical for grid reliability, but their performance degrades due to contamination (e.g., salt, soot, excrement). Traditional visual inspection methods are subjective, risky, and time-consuming. To overcome these challenges, this paper proposes an efficient and accurate framework for classifying insulator materials (glass, porcelain, composite) and contamination types. The framework employs features extracted independently from three lightweight CNNs: MobileNetV2, ShuffleNet, and EfficientNet-B0. These features are then fed into base classifiers, and their outputs (scores) are combined using various score-level fusion rules (Majority Vote, Maximum, Average, Sum, Minimum, Product) to enhance classification accuracy and robustness. The framework's effectiveness is validated on three datasets, including synthetic contamination scenarios and real-world images from the Merged Public Insulator Dataset (MPID). Results demonstrate that score fusion significantly outperforms individual lightweight models, achieving accuracies up to 98.49% for contamination classification, 98.59\% for combined material/contamination classification, and 99.26% for real-world material identification. Comparative analysis demonstrates significant improvements over existing methods, including VGG16 (97.00%) and custom CNNs (98.00%), highlighting the efficacy of feature and score fusion. The results validate the framework’s adaptability to diverse environments, computational efficiency, and potential for deployment in resource-constrained settings.
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