Image Processing Based Insulator Fault Detection Method

Tudevdagva, Uranchimeg; Battseren, Batbayar; Hardt, Wolf‐Dietrich; Troshina, Galina

doi:10.1109/apeie.2018.8545429

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…With the construction of the smart grid in full swing, the length of overhead transmission lines is increasing. As the demands for intelligent transmission line inspections continue to increase, drones are increasingly replacing manual labor for these inspections [3][4][5]. However, a large number of images collected by UAV inspection are mainly inspected manually or using traditional image processing techniques, which are inefficient and have poor detection accuracy [6,7].…”

Section: Introductionmentioning

confidence: 99%

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

Li,

Ouyang,

Chen

et al. 2024

ijacsa

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During UAV inspections of transmission lines, inspectors often encounter long distance and obstructed targets. However, existing detection algorithms tend to be less accurate when trying to detect these targets. Existing algorithms perform inadequately in handling long-distance and occluded targets, lacking effective detection capabilities for small objects and complex backgrounds. Therefore, we propose an improved YOLOv8-based YOLO-T algorithm for detecting multiple targets on transmission lines, optimized using transfer learning. Firstly, the model is lightweight while ensuring detection accuracy by replacing the original convolution block in the C2f module of the neck network with Ghost convolution. Secondly, to improve the target detection ability of the model, the C2f module in the backbone network is replaced with the Contextual Transformer module. Then, the feature extraction of the model is improved by integrating the Attention module and the residual edge on the SPPF (Spatial Pyramid Pooling-Fast). Finally, we introduce a new shallow feature layer to enable multi-scale feature fusion, optimizing the model detection accuracy for small and obscured objects. Parameters and GFLOPs are conserved by using the Add operation instead of the Concat operation. The experiment reveals that the enhanced algorithm achieves a mean detection accuracy of 97.19% on the transmission line dataset, which is 2.03% higher than the baseline YOLOv8 algorithm. It can also effectively detect small and occluded targets at long distances with a high FPS (98.91 frames/s).

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Section: Introductionmentioning

confidence: 99%

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

Li,

Ouyang,

Chen

et al. 2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…In the past, various intelligent detection methods based on images have been studied. The proposed detection methods can be divided into feature extraction methods [8][9][10], deep learning methods [6,[11][12][13], and deep learning and feature extraction combined methods [14][15][16]. For example, a Harris corner matching and spectral clustering method is proposed to achieve fault detection of catenary insulators [9], but this method is easily affected by image brightness and complex environmental background.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

2022

Applied Sciences

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The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.

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“…In the notice of the three-year action plan, China proposed promoting the mutual promotion of the real economy and artificial intelligence technology [6,7]. With the development of the intelligent industry, deep learning technology has begun to emerge in smart grid image recognition and defect detection applications [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

Chen

Wang

Shen

et al. 2021

Scientific Programming

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Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

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Image Processing Based Insulator Fault Detection Method

Cited by 16 publications

References 5 publications

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

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

Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

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