Image Identification Method of Ice Thickness on Transmission Line Based on Visual Sensing

Hu, Minghe; He, Jiancang; Alsabaan, Maazen

doi:10.1007/s11036-023-02244-1

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [20] proposed an ice thickness identification method based on the least squares Hough transform, which combines the Hough transform with the least squares method to identify the edges of the ice-covered transmission lines and then calculate the ice thickness. Hu et al [21] first used the K-Singular Value Decomposition (K-SVD) algorithm for image denoising, then used pixels to distinguish the ice-covered area, and performed pixel statistics to calculate the ice thickness. Traditional methods perform well in simple scenarios, but they cannot effectively identify ice thickness in complex real-world environments.…”

Section: Introductionmentioning

confidence: 99%

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness

Zhang,

Dou,

Jiao

et al. 2024

Sensors

View full text Add to dashboard Cite

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder’s ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

show abstract

Section: Introductionmentioning

confidence: 99%