A three-step synthetic extraction algorithm for transmission lines

2019 ◽  
Vol 233 (17) ◽  
pp. 6218-6228
Author(s):  
Dongsheng Zhang ◽  
Jing Wang ◽  
Rongjuan Han ◽  
Aifen Tian
Keyword(s):  
Feature Extraction ◽  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Detection Efficiency ◽  
Step Method ◽  
Sobel Operator ◽  
Extraction Algorithm ◽  
Aerial Vehicle ◽  
Line Crossing

The combination of vision and unmanned aerial vehicle is an economic and effective solution for transmission line inspection. The key in the process of transmission line inspection is real-time extraction and recognition of transmission lines regardless of the complicated background. The horizontal and vertical disturbances are numerous in the complicated natural background. At the same time, transmission line crossing and different shooting angles of unmanned aerial vehicle lead to the phenomenon of overlapping lines, which makes it more difficult to extract and track transmission lines. Therefore, a three-step synthetic extraction algorithm for transmission lines is proposed in the article. Firstly, in view of the edge extraction problem of the image, a complementary edge feature extraction algorithm combining the Sobel operator with the Log operator is proposed to solve the problems such as discontinuous and uneven edges and low-detection efficiency caused by the general Canny operator for edge detection. Secondly, in view of the line feature extraction, a coarse-fine directional optimization algorithm is proposed. The horizontal and vertical interferences of the extraction of transmission lines under complex background and the problem of line crossing in the image are eliminated. The algorithm has higher detection effect and efficiency than Hough transform. Finally, a similarity determination mechanism is proposed to merge the adjacent lines, which avoids the loss of the tracking line caused by overlapping lines in the image. A quadrotor unmanned aerial vehicle is used to carry out the experiment of transmission line detection in the article. The experimental results demonstrate that the three-step method of line extraction is more effective and efficient than the current algorithms.

scholarly journals A monocular-based navigation approach for unmanned aerial vehicle safe and autonomous transmission-line inspection

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982994 ◽  
Author(s):  
Xiaolong Hui ◽  
Jiang Bian ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Spatial Distance ◽  
Vanishing Point ◽  
Transmission Tower ◽  
Flight Direction ◽  
Field Environment ◽  
Smooth Tracking ◽  
Aerial Vehicle

This article presents a monocular-based navigation approach for unmanned aerial vehicle safe and continuous inspection along one side of transmission lines. To this end, a navigation model based on the transmission tower and the transmission-line vanishing point was proposed, and the following three key issues were addressed. First, a deep-learning-based object detection and a fast and smooth tracking algorithm based on the kernelized correlation filter were combined to locate transmission tower timely and reliably. Second, the vanishing point of transmission lines was computed and optimized to provide unmanned aerial vehicle with a robust and precise flight direction. Third, to keep a stable safe distance from transmission lines, the transmission lines were first rectified by optimizing a homography matrix to eliminate the parallel distortion, and then their interval variation was estimated for reflecting the spatial distance variation. Finally, the real distance from transmission tower was measured by the triangulation across multiple views. The proposed navigation approach and the designed UAV platform were tested in a field environment, which achieved an encouraging result. To the best of authors’ knowledge, this article marks the first time that a safe and continuous navigation approach along one side of transmission lines is put forward and implemented.

scholarly journals Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 472-478 ◽  
Author(s):  
Wenfei Xi ◽  
Zhengtao Shi ◽  
Dongsheng Li
Keyword(s):  
Feature Extraction ◽  
Unmanned Aerial Vehicle ◽  
Practical Case ◽  
Extraction Technology ◽  
Diverse Environment ◽  
Extraction Algorithm ◽  
Aerial Vehicle ◽  
Uav Image ◽  
Forstner Operator ◽  
Speed And Accuracy

AbstractFeature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV), this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

scholarly journals Research on small lightweight electromagnetic interference measurement system based on patrol unmanned aerial vehicle

2021 ◽  
Vol 2108 (1) ◽  
pp. 012011
Author(s):  
Pingyuan Liu ◽  
Nian Wu ◽  
Junwen Yao ◽  
Xianbin Ke ◽  
Shiguang Bie ◽  
...  
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Measurement System ◽  
Transmission Lines ◽  
Electromagnetic Interference ◽  
Full Spectrum ◽  
Observation Methods ◽  
Interference Measurement ◽  
Aerial Vehicle ◽  
Infrared Cameras

Abstract Unmanned aerial vehicle (UAV) has been widely used in power system inspection. Most of the existing UAVs are equipped with visible and infrared cameras for fast full spectrum photography and fault monitoring of transmission lines. When the transmission line has defects such as aging, broken strand and serious corrosion, the traditional image recognition, infrared and other observation methods can not identify these defects well. In order to realize the rapid detection and judgment of broken strand, this paper analyzes the characteristics of electromagnetic interference of transmission line, and constructs the electromagnetic interference measurement system based on patrol UAV. The research results of this paper can realize the real-time detection and alarm of broken strand, and provide technical support for line inspection.

Power Transmission Lines Inspection using Properly Equipped Unmanned Aerial Vehicle (UAV)

2018 ◽  
Author(s):  
Alexandros Zormpas ◽  
Konstantia Moirogiorgou ◽  
Kostas Kalaitzakis ◽  
George A. Plokamakis ◽  
Panayotis Partsinevelos ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Aerial Vehicle

scholarly journals Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 1928
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer ◽  
Adel Manaa Dakhil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Transmission Line ◽  
Convolutional Neural Networks ◽  
Transmission Lines ◽  
Fault Classification ◽  
Line System ◽  
Direction Discrimination ◽  
Conventional Methods

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

scholarly journals Detection method based on improved faster R-CNN for pin defect in transmission lines

2021 ◽  
Vol 300 ◽  
pp. 01011
Author(s):  
Jun Wu ◽  
Sheng Cheng ◽  
Shangzhi Pan ◽  
Wei Xin ◽  
Liangjun Bai ◽  
...  
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Aerial Vehicle ◽  
The Stability ◽  
Small Targets

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

scholarly journals Reliability Study of Important Cross-Over Transmission Line System Based on Series System

2021 ◽  
Vol 2137 (1) ◽  
pp. 012028
Author(s):  
Dengjie Zhu ◽  
Yongli Liao ◽  
Hao Li ◽  
Jie Tang ◽  
Zenghao Huang ◽  
...  
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Calculation Method ◽  
Reliability Index ◽  
Force Transmission ◽  
Line System ◽  
Reliability Calculation ◽  
Safety And Reliability ◽  
The Cross ◽  
Line Crossing

Abstract Transmission lines inevitably cross railroads, highways, and other facilities, and in order to ensure the safety and reliability of the cross-crossing section, the influence of various random factors on important cross-crossing transmission lines needs to be fully considered. In this paper, the transmission line crossing section is treated as a tandem system according to the force transmission route, and the reliability calculation method of the cross-crossing transmission line system based on the tandem system is proposed. The reliability calculation method of each component and the reliability calculation method of the tandem system are given. Finally, an example of the reliability calculation of a 220kV cross-crossing transmission line system is given, and the results show that the cross-crossing section has the highest reliability of tower FSJ404, with a reliability index of 8.61, the second highest reliability of insulators, with a reliability index of 7.22, and the lowest reliability of tower FSJ302, with a reliability index of 4.28. The failure probability of the cross-crossing section is 0.000009245, and the reliability index is 4.28.

scholarly journals Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
MUHAMMAD EFAN ABDULFATTAH ◽  
LEDYA NOVAMIZANTI ◽  
SYAMSUL RIZAL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Super Resolution ◽  
Image Features ◽  
Scale Factor ◽  
Aerial Imagery ◽  
Aerial Vehicle

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution

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