Journal ArticleDOI
Intelligent fault detection of high voltage line based on the Faster R-CNN
Xusheng Lei,Zhehao Sui +1 more
TLDR
A deep convolution neural network method based on Faster R-CNN method is proposed to locate the broken insulators and bird nests in the electric power line using the ResNet-101 network model.About:
This article is published in Measurement.The article was published on 2019-05-01. It has received 87 citations till now. The article focuses on the topics: Fault detection and isolation.read more
Citations
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Journal ArticleDOI
A Review on Deep Learning Applications in Prognostics and Health Management
TL;DR: The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data and suggests the possibility of transfer learning across PHM applications.
Journal ArticleDOI
State of the Art in Defect Detection Based on Machine Vision
TL;DR: A detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms.
Journal ArticleDOI
Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
TL;DR: Three novel Deep Learning classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP) are introduced.
Journal ArticleDOI
Research on Recognition Method of Electrical Components Based on YOLO V3
TL;DR: A way to detect the electrical components in the Unmanned Aerial Vehicle (UAV) inspection image based on You Only Look Once (YOLO) V3 algorithm is proposed and reaches high recognition accuracy, good robustness, and strong real-time performance for UAV power inspection system.
Journal ArticleDOI
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model
TL;DR: The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.