Zhao et al., 2023 - Google Patents

Transmission Line Fault Identification Method based on Attention Mechanism and Deep Neural Network

Zhao et al., 2023

Document ID
15123218310752252315
Author
Zhao C
Zhang Y
Xie B
Publication year
Publication venue
2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

External Links

Snippet

In response to the problem of traditional algorithms being difficult to accurately detect unmanned aerial vehicles (UAVs) during line inspection due to complex scenes, a transmission line fault recognition method based on attention mechanism and deep neural …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6288Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory

Similar Documents

Publication Publication Date Title
CN106595551B (en) Ice covering thickness detection method in powerline ice-covering image based on deep learning
CN111428748B (en) HOG feature and SVM-based infrared image insulator identification detection method
Liu et al. High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines
Sohn et al. Automatic powerline scene classification and reconstruction using airborne lidar data
CN110033453A (en) Based on the power transmission and transformation line insulator Aerial Images fault detection method for improving YOLOv3
CN112150412A (en) Insulator self-explosion defect detection method based on projection curve analysis
Wanguo et al. Typical defect detection technology of transmission line based on deep learning
Tao et al. Electric insulator detection of UAV images based on depth learning
Bai et al. Insulator fault recognition based on spatial pyramid pooling networks with transfer learning (match 2018)
Özer et al. An approach based on deep learning methods to detect the condition of solar panels in solar power plants
Zhao et al. Transmission Line Fault Identification Method based on Attention Mechanism and Deep Neural Network
Hong-Bin et al. Target tracking method of transmission line insulator based on multi feature fusion and adaptive scale filter
Yang et al. Abnormal Object Detection with an Improved YOLOv8 in the Transmission Lines
Lv et al. Research on Insulator Defect Detection and Recognition in Overhead Transmission Line Based on Convolutional Neural Network
Zhouhua et al. Multi-target defect intelligent recognition of transmission line based on improved Faster-RCNN
CN113989209B (en) Power line foreign matter detection method based on Faster R-CNN
CN116385950A (en) Electric power line hidden danger target detection method under small sample condition
Sheng et al. A Method and Implementation of Transmission Line's Key Components and Defects Identification Based on YOLO
Wu et al. Detection method based on improved faster R-CNN for pin defect in transmission lines
Xuan et al. UAV transmission line inspection algorithm based on cross-scale feature fusion and attention mechanism
Zhao et al. SceneNet: A Multi-Feature Joint Embedding Network With Complexity Assessment for Power Line Scene Classification
Su et al. Automatic detection method for iced transmission lines under complex background
Zhang et al. Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN
Shi et al. Tree Growth Simulation and Safe Distance Calculation Algorithm Design of Transmission Channel Based on Deep Learning
Zhang et al. A Fault Detection Method for Power Transmission Lines Using Aerial Images