Research on Small-scale Defect Identification and Detection of Smart Grid Transmission Lines Based on Image Recognition

Cao, Gang; Liu, Yong; Fan, Zhi; Chen, Liang; Mei, Hao; Zhang, Shaochuan; Wang, Junyu; Han, XW

doi:10.1109/auteee52864.2021.9668671

Cited by 3 publications

(1 citation statement)

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“…Michalski [10], CaoG [11], and LiHC [12] et al manually annotated the location and category of transmission towers in satellite images to implement the classification and naming of towers. LiuJ [13], XuCL [14], LiuZY [15], and LiaoY [16] et al developed an intelligent annotation platform to mark inspection images which is conducive to subsequent line maintenance operations by operation and maintenance personnel.…”

Section: Automatic Naming Of Inspection Imagesmentioning

confidence: 99%

Stower-13: A Multi-View Inspection Image Dataset for the Automatic Classification and Naming of Tension Towers

Lu,

Zheng,

Chen

et al. 2024

Electronics

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In high-voltage transmission lines, the tension tower needs to withstand the tension load of the overhead power line for a long time, which is prone to damage, and it is an important part of the inspection in the circuit inspection. In the modern circuit inspection process, operation and maintenance personnel mostly use unmanned aerial vehicles (UAVs) to photograph various parts of the tension tower, obtain inspection images, and manually classify and name the massive inspection images, which is low in accuracy and efficiency. Based on the above problems, this paper collects a large number of real-life UAV inspection images of various parts of a tension tower, and proposes the Stower-13 inspection image dataset, which is used to train the classification model to achieve automatic classification and naming of inspection images. Based on this dataset, this paper also proposes an improved MobileViT model, in which the Scale-Correlated Pyramid Convolution Block Attention Block (SCPCbam) module is introduced, which adds the Convolution Block Attention Module (CBAM) to the four branches of the original Scale-Correlated Pyramid Convolution (SCPC) module, so as to strengthen the ability of image multi-scale information extraction and improve the classification accuracy. This paper discusses a number of experiments on the model, and the experimental results show that the dataset proposed in this paper helps the model to understand the feature information. At the same time, the improved MobileViT model has a strong ability to extract image spatial feature information, the classification accuracy is higher than that of other models of the same type, so it is able to cope with a wide range of problems that arise in the course of practice, and it meets the practical needs of automatically naming transmission line inspection images.

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Section: Automatic Naming Of Inspection Imagesmentioning

confidence: 99%

Stower-13: A Multi-View Inspection Image Dataset for the Automatic Classification and Naming of Tension Towers

Lu,

Zheng,

Chen

et al. 2024

Electronics

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Smart Power Safety Hazard Inspection System Based on YOLOv7

Liang,

Li,

Deng

2024

Communications in Computer and Information Science

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Research on the Identification Method of Overhead Transmission Line Breeze Vibration Broken Strands Based on VMD−SSA−SVM

Wang

et al. 2022

Electronics

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For the problem of overhead line broken strand identification, a VMD−SSA−SVM-based overhead transmission line broken strand identification method is proposed. First, the vibration acceleration response signal of overhead transmission line under breeze environment was simulated by Ansys simulation software. Then the variable modal decomposition of the vibration acceleration response of the transmission line is performed to obtain the intrinsic mode function corresponding to the vibration behavior of the transmission line. The Hilbert transform is used to analyze the intrinsic mode functions of each order, and the Hilbert spectrum and marginal spectrum of the intrinsic mode functions of each order are studied to extract the first six order intrinsic frequencies. Each set of intrinsic frequencies is input as a feature vector into a support vector machine optimized by the sparrow search algorithm for training to achieve the identification of broken strands in overhead transmission lines. This paper studies the inherent frequency changes and identification results of overhead transmission lines under two states of normal operation and broken 5 strands operation, and the test results show that the identification method based on VMD-SSA-SVM has good identification effect on broken strands of overhead lines.

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Research on Small-scale Defect Identification and Detection of Smart Grid Transmission Lines Based on Image Recognition

Cited by 3 publications

References 0 publications

Stower-13: A Multi-View Inspection Image Dataset for the Automatic Classification and Naming of Tension Towers

Stower-13: A Multi-View Inspection Image Dataset for the Automatic Classification and Naming of Tension Towers

Smart Power Safety Hazard Inspection System Based on YOLOv7

Research on the Identification Method of Overhead Transmission Line Breeze Vibration Broken Strands Based on VMD−SSA−SVM

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