Insulator Surface Dirt Image Detection Technology Based on Improved Watershed Algorithm

Mei, Xin; Lü, Tao; Wu, Xiaojie; Zhang, Bo

doi:10.1109/appeec.2012.6307691

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…The basic components of the electricity transmission system or grids include transmission power line, transmission towers, insulators, balisor beacon, sag adjusters, etc., play an important role in the safe transmission of electricity between a grid station and a local distribution station [1]. These components bear extreme weather conditions (direct exposure to sunlight all day, strong winds, snow, rain, etc.…”

Section: Introductionmentioning

confidence: 99%

A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Siddiqui

Park

2020

Energies

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Defects in high voltage transmission line components such as cracked insulators, broken wires rope, and corroded power line joints, are very common due to continuous exposure of these components to harsh environmental conditions. Consequently, they pose a great threat to humans and the environment. This paper presents a real-time aerial power line inspection system that aims to detect power line components such as insulators (polymer and porcelain), splitters, damper-weights, power lines, and then analyze these transmission line components for potential defects. The proposed system employs a deep learning-based framework using Jetson TX2 embedded platform for the real-time detection and localization of these components from a live video captured by remote-controlled drone. The detected components are then analyzed using novel defect detection algorithms, presented in this paper. Results show that the proposed detection and localization system is robust against highly cluttered environment, while the proposed defect analyzer outperforms similar researches in terms of defect detection precision and recall. With the help of the proposed system automatic defect analyzing system, manual inspection time can be reduced.

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Section: Introductionmentioning

confidence: 99%

A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Siddiqui

Park

2020

Energies

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“…Pernebayeva [4] uses Gabor and standard deviation filters with the k-NN classifier to assess the surface condition of the insulator by determining the presence of ice, snow, and water using images. It may [5] used the DAS algorithm of image processing to detect surface impurities on the insulator. Oberweger [6] suggested a method based on local gradient-based descriptors to take the isolator cap spontaneously, and then use elliptical spatial support to analyze automatically possible errors.…”

Section: Introductionmentioning

confidence: 99%

Investigasi Kondisi Permukaan Isulator Tegangan Tinggi berbasis Machine Learning TensorFlow

Manjang

2020

Intek

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The insulator plays an essential role in preventing the flow of current from the phase conductor to the earth through supporting towers so that the insulation is a significant part of the electrical energy transmission system. Generally, high-voltage insulators are widely used as external plug insulators, for that the performance of insulators is influenced by environmental conditions that indirectly affect the surface condition of the insulators. In this study, a diagnostic tool used in the testing surface of the insulator, which can classify mechanically whether the insulator is good or damaged. The classification method uses TensorFlow Machine learning. Machine Learning is used as a brain in the isolation classification process while TensorFlow functions to store training data and test data in the classification process. The results obtained from this study show the accuracy of classification data is 98%.

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“…Powerline equipment such as insulators, cut-out-switches, and lightning arresters plays vital roles, on either the power generation or the distribution side, for the safe delivery of electricity to the end user [ 1 ]. Since these equipment has to bear severe weather conditions, high mechanical tension, and extreme voltage power, they are easily damaged, in which case they must be replaced or repaired before their electrical life end [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most salient features of the proposed scheme is the ability to detect 17 different types of electrical equipment. Moreover, most of the existing methods present naïve approaches of thresholding the color or intensity image using a single threshold [ 1 , 14 , 15 , 16 , 17 , 25 , 26 , 33 , 34 , 35 , 36 ], and hence these methods are sensitive to color and lighting variations. In order to show the robustness of the our proposed method, we evaluated our method on a large dataset of 644 cluttered insulator images, while the methods described by [ 17 , 22 , 24 , 26 , 33 , 36 ] used small datasets of 2, 3, 4, 5, 10, and 74 images, respectively, and the methods in [ 2 , 21 ] tested their algorithm on a single image.…”

Section: Introductionmentioning

confidence: 99%

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

Siddiqui

Park

Lee

et al. 2018

Sensors

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Electric power line equipment such as insulators, cut-out-switches, and lightning-arresters play important roles in ensuring a safe and uninterrupted power supply. Unfortunately, their continuous exposure to rugged environmental conditions may cause physical or electrical defects in them which may lead to the failure to the electrical system. In this paper, we present an automatic real-time electrical equipment detection and defect analysis system. Unlike previous handcrafted feature-based approaches, the proposed system utilizes a Convolutional Neural Network (CNN)-based equipment detection framework, making it possible to detect 17 different types of powerline insulators in a highly cluttered environment. We also propose a novel rotation normalization and ellipse detection method that play vital roles in the defect analysis process. Finally, we present a novel defect analyzer that is capable of detecting gunshot defects occurring in electrical equipment. The proposed system uses two cameras; a low-resolution camera that detects insulators from long-shot images, and a high-resolution camera which captures close-shot images of the equipment at high-resolution that helps for effective defect analysis. We demonstrate the performances of the proposed real-time equipment detection with up to 93% recall with 92% precision, and defect analysis system with up to 98% accuracy, on a large evaluation dataset. Experimental results show that the proposed system achieves state-of-the-art performance in automatic powerline equipment inspection.

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Insulator Surface Dirt Image Detection Technology Based on Improved Watershed Algorithm

Cited by 12 publications

References 3 publications

A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Investigasi Kondisi Permukaan Isulator Tegangan Tinggi berbasis Machine Learning TensorFlow

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

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