Next Article in Journal
An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver
Previous Article in Journal
Bistatic Forward-Looking SAR Moving Target Detection Method Based on Joint Clutter Cancellation in Echo-Image Domain with Three Receiving Channels
Article

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

1
Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea
2
Department of Electronic Engineering, NED University of Engineering & Technology, Karachi-75270, Pakistan
3
Department of Software, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam 13120, Korea
4
Smart Distribution Lab. SW Platform Center, KEPCO Research Institute, 105 Moonji-ro, Daejeon 34056, Korea
5
ST Vision, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea
*
Author to whom correspondence should be addressed.
Received: 4 October 2018 / Revised: 3 November 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
(This article belongs to the Section Sensor Networks)
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. View Full-Text
Keywords: convolutional neural networks; deep learning; powerline equipment; insulators; cut-out-switches; computer vision; defect analysis; gunshot damage; ellipse detection; electrical safety convolutional neural networks; deep learning; powerline equipment; insulators; cut-out-switches; computer vision; defect analysis; gunshot damage; ellipse detection; electrical safety
Show Figures

Figure 1

MDPI and ACS Style

Siddiqui, Z.A.; Park, U.; Lee, S.-W.; Jung, N.-J.; Choi, M.; Lim, C.; Seo, J.-H. Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network. Sensors 2018, 18, 3837. https://0-doi-org.brum.beds.ac.uk/10.3390/s18113837

AMA Style

Siddiqui ZA, Park U, Lee S-W, Jung N-J, Choi M, Lim C, Seo J-H. Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network. Sensors. 2018; 18(11):3837. https://0-doi-org.brum.beds.ac.uk/10.3390/s18113837

Chicago/Turabian Style

Siddiqui, Zahid A., Unsang Park, Sang-Woong Lee, Nam-Joon Jung, Minhee Choi, Chanuk Lim, and Jang-Hun Seo. 2018. "Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network" Sensors 18, no. 11: 3837. https://0-doi-org.brum.beds.ac.uk/10.3390/s18113837

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop