A case-aware feature modulation framework for defect classification in power lines

Conductors and grounded transmission towers are separated by non-conductive overhead transmission line insulators are known as materials. They frequently meet with problems once they are put into use mechanical or electrical pressure and environmental pollution. It is important to carry out regular inspections to avoid power failures because adverse working conditions may lead to insulation breakdown. To do this, this study proposes a new method of classifying high-voltage surface conditions of insulators are given depending on the picture, which is founded on deep convolutional neural networks (CNNs). We suggest MS-CADFM-SSL, a new multi-task model of defect classification of various components of power lines. The strategy incorporates a communal EfficientNet Multi-scale case-dependent dynamic feature modulation, orthogonality regularization, backbone with multi-scale case-dependent dynamic feature modulation and self-supervised pretraining to learn jointly generalized representations and keep task specific discriminative features. The framework was tested on five nonhomogeneous defect cases has strong performance with regard to precision, recall, F1-score, and accuracy. Industrially, it guarantees dependable identification of severe errors, decreases computing expenses, and real time checking of the transmission lines. In comparison to traditional single-task or naive multitask.MS-CADFM-SSL has better adaptability to visually diverse and imbalanced datasets, where focus areas are physically meaningful due to Grad-CAM visualizations. Despite difficulties using infrequent or delicate anomalies and depending on fixed images, the structure offers a scalable basis of automated defect test. Future extensions consist of multimodal and temporal integration of data, semi-supervised learning and predictive maintenance prioritization to improve dependability and workability.