Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms

Li, Teng; Tian, Hao

doi:10.3390/app12105004

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…The geometric moment, Legendre moment, Hermite moment, and Zernike moment are commonly used. Compared with other moments, the operation is simpler [ 32 ]. Fritz Zernike (1888–1966) introduced a group of complex polynomials defined on the unit circle in 1934, which has completeness and orthogonality, so it can represent any square integrable function defined in the unit circle.…”

Section: Principle and Methods Of Mtf Measurementmentioning

confidence: 99%

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

Zhang¹,

Wang²,

Wu³

et al. 2023

Sensors

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The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional methods under the conditions of noise and blur, this paper proposes a new method of MTF measurement with a slanted-edge method based on improved Zernike moments, which firstly introduces the Otsu algorithm to automatically determine the Zernike moment threshold for sub-pixel edge detection to precisely locate the edge points, then obtains LSF through edge point projection, ESF sampling point acquisition, smoothing, fitting, taking ESF curve differential and Gaussian fitting, and finally, accurately obtaining MTF by LSF Fourier transform and modulo normalization. Based on simulation experiments and outdoor target experiments, the reliability of the proposed algorithm is verified by the deviations of slanted-edge angle and MTF measurement, and the tolerance degree of edge detection to noise and ambiguity are analyzed. The results show that compared with ISO 12233, OMNI-sine method, Hough transform method and LSD method, this algorithm has the highest edge detection accuracy, the maximum tolerance of noise and ambiguity, and also improves the accuracy of MTF measurement.

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Section: Principle and Methods Of Mtf Measurementmentioning

confidence: 99%

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

Zhang¹,

Wang²,

Wu³

et al. 2023

Sensors

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show abstract

“…After the softmax operation, the cross-channel soft attention module is used to adaptively select information at different spatial scales. The calculation process is shown as Formulas (1)- (5).…”

Section: Sk Attention Modulementioning

confidence: 99%

“…Finally, the output feature, 𝑉, obtained according to the attention weights of different cores, is shown in Formula (5).…”

Section: Sk Attention Modulementioning

confidence: 99%

“…In pursuit of the intelligent detection of catenary defects, many scholars have studied the detection of various components of high-speed rail catenaries, such as insulator recognition and defect detection [4,5], loose nut detection in U-shaped hoops [6], bird's nest detection [7], loose defect detection for bracing wire components [8], and arc detection and recognition in pantograph-catenary systems [9]. At present, research on catenary droppers tends to focus on engineering structures and geometric parameters, as well as the impact of the suspension string design on train operation safety at the physical level.…”

Section: Introductionmentioning

confidence: 99%

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YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Rao

Liu

et al. 2023

Applied Sciences

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High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.

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“…Li vd. [14] izolatör kusurlarının tespiti için derin öğrenme ve Zernike moment algoritmalarını birleştiren bir yöntem önermişlerdir. İzolatörleri tanımak için R-CNN algoritması kullanmışlardır.…”

Section: Introductionunclassified

Detection of Insulator Faults in Catenary Systems with Deep Learning

Karaduman

Akın

BİNAY

et al. 2022

Demiryolu Mühendisliği

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Insulators are the most important components of catenary systems in electrified railway lines. Fractures or burns in insulators cause interruptions in transportation. These interruptions also prevent safe operation, especially on high-speed rail lines. Detecting faults in insulators at an early stage will enable to intervene in catenary systems at the most appropriate time and prevent insulator-related accidents. In this article, a deep learning-based method is proposed to classify insulators in catenary systems as faulty or intact. A data set containing 1100 isolator images was used in the study. The images in this dataset are trained and tested with the ResNet34 deep learning architecture. With the proposed architecture, faults in isolators are classified with 95,7% accuracy, 99% precision and 96,6% recall values. These values show that the performed study is a reliable method for fault detection in isolators in catenary systems.

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Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms

Cited by 8 publications

References 35 publications

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments

YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Detection of Insulator Faults in Catenary Systems with Deep Learning

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