Detection of Hydrophobicity Grade of Composite Insulators Based on MDC‐YOLO Algorithm

ABSTRACT In the field of power equipment inspection, the aging condition of composite insulators is often determined by the detection of water repellency. However, the existing detection methods are difficult to effectively extract the water repellency level features in the complex background, and it is difficult to meet the real‐time requirements. Therefore, this paper proposes a MDC‐YOLO algorithm for water repellency detection and classification of composite insulators. The algorithm effectively combines the efficient convolutional network (EfficientNetV2), the deformable attention mechanism (DAttention), and the lightweight convolution (CSPPC), which significantly realizes the lightweight of the network, and at the same time improves the accuracy of the algorithm for the composite insulator umbrella skirt identification, water repellency class classification. Experimentally verified, the multilayer detection and classification YOLO algorithm of water repellency of composite insulators proposed in this paper, i.e., MDC‐YOLO algorithm, improves the detection accuracy of umbrella skirt by 5.19% and reduces the GFLOPs to 3.5, and improves the Top‐1 accuracy of water repellency grade classification by 4.654% and reduces the GFLOPs by 1.1. The results of the present research can be widely applied to The results of this study can be widely used in the fields of power equipment inspection, smart grid construction and insulator aging state assessment, which provides strong support for the technical development of related fields. It meets the requirements of hydrophobicity detection and classification of composite insulators, and proves the effectiveness and superiority of the algorithm proposed in this paper through ablation and comparison tests.