Recognition of Hydrophobicity Class of Polymeric Insulators Employing Residual Morphological Neural Network and Granulometry-Based Image Analysis

Chatterjee, Soumya; Roy, Sayanjit Singha; Chatterjee, Arpan; Ganguly, Biswarup; Paul, Subho

doi:10.1109/tim.2022.3164156

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Du et al [8] used digital image processing technology and the fractal dimension method to establish the relationship between discharge light pattern characteristics and HC level to find the mathematical expression of insulator surface hydrophobic level. Chatterjee et al [9] studied the variation of particle morphology with the hydrophobic grade of polymer insulators by distinguishing different insulator hydrophobic grades based on multi-scale mathematical morphology. Given the shortcomings of traditional hydrophobic image feature representation methods, the application of deep learning technology in insulator hydrophobic detection began to appear.…”

Section: School Of Electronics and Information Xi'an Polytechnic Univ...mentioning

confidence: 99%

“…Chatterjee et al . [9] studied the variation of particle morphology with the hydrophobic grade of polymer insulators by distinguishing different insulator hydrophobic grades based on multi‐scale mathematical morphology. Given the shortcomings of traditional hydrophobic image feature representation methods, the application of deep learning technology in insulator hydrophobic detection began to appear.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Song

Huang

et al. 2023

IEEJ Transactions Elec Engng

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The hydrophobia of composite insulators is a parameter closely related to insulation performance. As an essential index to measure the pollution flashover resistance of composite insulators, the hydrophobic grade is of great significance to maintaining transmission line insulators. To judge the hydrophobic grade of composite insulators accurately and efficiently and to solve the problem of difficult extraction of fine‐grained feature information and high similarity between classes in the data sets of anti‐hydrophobic grade recognition, an end‐to‐end intelligent identification method of the hydrophobic grade of composite insulators based on Efficient GA‐YOLO Former network is proposed. This method introduces Swin Transformer—a multi‐head self‐attention mechanism with shifted windows, into CNN, which helps the model better understand the multi‐scale global semantic information of images through cross‐layer interaction. The efficient BiFPN structure is introduced in the neck part to realize the bidirectional cross‐scale connection and weighted feature fusion in the feature extraction process. By integrating the GAM attention mechanism to amplify the significant cross‐dimensional acceptance region, the model can learn more subtle details that are more difficult to distinguish, improving model efficiency and detection accuracy. Combined with the target detection algorithm, a system for evaluating the hydrophobic grade of composite insulators on transmission lines is designed, which can realize the real‐time measurement of the hydrophobic grade of composite insulators and has practical engineering application value. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: School Of Electronics and Information Xi'an Polytechnic Univ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Song

Huang

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

Hydrophobicity classification of polymeric insulators using a masked autoencoder model in vision transformer

Panigrahy,

Karmakar

2024

Computers and Electrical Engineering

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Hydrophobicity Grade Detection of Polymeric Insulator Using Image Visibility Graph

Roy

Paramane

Singh

et al. 2022

2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)

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Recognition of Hydrophobicity Class of Polymeric Insulators Employing Residual Morphological Neural Network and Granulometry-Based Image Analysis

Cited by 11 publications

References 19 publications

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Hydrophobicity classification of polymeric insulators using a masked autoencoder model in vision transformer

Hydrophobicity Grade Detection of Polymeric Insulator Using Image Visibility Graph

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