Evaluation method of hydrophobicity of composite insulators based on improved Mask R-CNN
To ensure the safe operation of power system, it is necessary to discriminate the hydrophobicity level online of composite insulators in time. In order to improve the generalization ability of composite insulator hydrophobicity state evaluation model, and solve the problem that the existing classifi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-04-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220525002&flag=1&journal_id=dcyyben&year_id=2025 |
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| Summary: | To ensure the safe operation of power system, it is necessary to discriminate the hydrophobicity level online of composite insulators in time. In order to improve the generalization ability of composite insulator hydrophobicity state evaluation model, and solve the problem that the existing classification models only focus on the parts with good hydrophobicity when evaluating the composite insulators with uneven hydrophobicity degree. In this paper, the classification problem is transformed into the target detection problem, and the improved mask region-based convolutional neural network (Mask R-CNN) algorithm is used to evaluate the hydrophobicity level of composite insulators. Firstly, the location and size of all water droplets in the image are determined by feature pyramid network (FPN) and the mask branch of Mask R-CNN is used to predict the hydrophobicity level of all water droplets. Then, the area of the corresponding hydrophobicity level is calculated. Finally, the hydrophobicity level with the largest area is selected as the classification results of the image. Especially, combined with the characteristics of each level of hydrophobicity image, we introduce the soft non-maximum suppression (Soft-NMS) structure to reduced the missed target detection because of the scale problem of water droplets and the irregular distribution in high level hydrophobicity images, and introduce generalized intersection over union loss (GIOU) to accelerate the convergence rate of the model with small and multi-temporal objects in low level images. Final validation through comparative experiments demonstrated the effectiveness and superiority of the enhanced Mask R-CNN-based hydrophobicity image recognition algorithm across three critical metrics: mean average precision (MAP), frames per second (FPS), and classification accuracy. |
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| ISSN: | 1001-1390 |