The method of insulator recognition based on deep learning

Liu, Yue; Yong, Jun; Liu, Liang; Zhao, Jinlong; Li, Zongyu

doi:10.1109/carpi.2016.7745630

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…In recent studies, deep learning-based object detectors have been widely applied in industrial scenarios related to the power system [21,22]. Existing methods usually exploit a two-stage process to locate the regions of power line strand breakage.…”

Section: Strand Breakage Detectionmentioning

confidence: 99%

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Yan,

Zhang,

Shen

et al. 2023

Drones

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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.

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Section: Strand Breakage Detectionmentioning

confidence: 99%

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Yan,

Zhang,

Shen

et al. 2023

Drones

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“…As a comparison, the CRF is added herein behind the compact network, which is masked as CRF‐Net. Fourth, the multi‐stage processing methods have many artificial thresholds [11, 12]. All networks were trained or fine‐tuned using the same samples in Section 3.2.…”

Section: Methodsmentioning

confidence: 99%

“…The mixed features were classified by SVM. In [12], a six‐layer convolution neural network (CNN) was used to extract the features of patches divided into three categories: background, tower, and insulators. However, the extracted features of a fixed sized sliding window were difficult to apply to multi‐scale insulators.…”

Section: Introductionmentioning

confidence: 99%

Real‐time segmentation of various insulators using generative adversarial networks

Chang

Yang

et al. 2018

IET Computer Vision

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The conventional inspection of fragile insulators is critical to grid operation and insulator segmentation is the basis of inspection. However, the segmentation of various insulators is still difficult because of the great differences in colour and shape, as well as the cluttered background. Traditional insulator segmentation algorithms need many artificial thresholds, thereby limiting the adaptability of algorithms. A compact end-to-end neural network, which is trained in the framework of conditional generative adversarial networks, is proposed for the real-time pixel-level segmentation of insulators. The input image is mapped to a visual saliency map, and various insulators with different poses are filtered out at the same time. The proposed two-stage training and empty samples are also used to improve the segmentation quality. Extensive experiments and comparisons are performed on many real-world images. The experimental results demonstrate superior segmentation and real-time performance. Meanwhile, the effectiveness of the proposed training strategies and the trade-off between performance and speed are analysed in detail.

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Position and Orientation Detection of Insulators in Arbitrary Direction Based on YOLOv3

Wang

Yan

et al. 2020

Lecture Notes in Computer Science

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The method of insulator recognition based on deep learning

Cited by 11 publications

References 6 publications

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Real‐time segmentation of various insulators using generative adversarial networks

Position and Orientation Detection of Insulators in Arbitrary Direction Based on YOLOv3

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