High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

Huang, Yourui; Jiang, Lingya; Xu, Shanyong; Liu, Yuwen; Fu, Jiahao

doi:10.3390/app122412682

Cited by 18 publications

(13 citation statements)

References 33 publications

(34 reference statements)

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“…The MaskRCNN model is characterized by simplicity and quality of prediction compared to other neural networks. The result of training based on the MaskRCNN neural network model is considered to be the best compared to neural networks with Backbone, Neck, Head layers [13,22,23], and the accuracy result showed 89 % (Fig. 9).…”

Section: Discussion Of the Results Of Research On The Use Of Computer...mentioning

confidence: 99%

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Identification of weeds in fields based on computer vision technology

Kaldarova,

Аkanova,

Nazyrova

et al. 2023

EEJET

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The problem of multiple zones in computer vision, including pattern recognition in the agricultural sector, occupies a special place in the field of artificial intelligence in the modern aspect. The object of the study is the recognition of weeds based on deep learning and computer vision. The subject of the study is the effective use of neural network models in training, involving classification and processing using datasets of plants and weeds. The relevance of the study lies in the demand of the modern world in the use of new information technologies in industrial agriculture, which contributes to improving the efficiency of agro-industrial complexes. The interest of private agricultural enterprises and the state is caused by an increase in the yield of agricultural products. To recognize weeds, machine learning methods, in particular neural networks, were used. The process of weed recognition is described using the Mark model, as a result of processing 1,562 pictures, segmented images are obtained. Due to the annual increase in weeds on the territory of Kazakhstan and in the course of solving these problems, a new plant recognition code was developed and written in the scanner software module. The scanner, in turn, provides automatic detection of weeds. Based on the results of a trained neural network based on the MaskRCNN neural network model written in the scanner software module meeting new time standards, the automated plant scanning and recognition system was improved. The weed was recognized in an average of 0.2 seconds with an accuracy of 89 %, while the additional human factor was completely removed. The use of new technology helps to control weeds and contributes to solving the problem of controlling them

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Section: Discussion Of the Results Of Research On The Use Of Computer...mentioning

confidence: 99%

“…This is because the model was evaluated without negative samples. The YOLO [13] and R-CNN [14] models differ in sensitivity and precision. The HOG-SVM model is shown to be very accurate on the basis of clarity [15].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Identification of weeds in fields based on computer vision technology

Kaldarova,

Аkanova,

Nazyrova

et al. 2023

EEJET

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“…In order to solve the interference of complex background on target detection, this paper adds SimAM, 34 CBAM, 35 and LS-Net to the backbone of the model for performance evaluation. The results are shown in Table 2.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

Liu,

2024

J. Electron. Imag.

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Quick and accurate detection of insulator defects from the complex aerial background (such as trees, hillsides, lakes, and buildings) is important work to ensure the safe operation of transmission lines. The existing detection methods have difficulty detecting the defect target due to the strong interference of complex backgrounds in aerial images. To solve this problem, we propose an insulator defect detection model based on a cascaded network. First, we introduce a hierarchical semantic segmentation network to separate the complex background from the target insulator, which is embedded into the main feature extraction branch to form a "segmentation-detection" cascade network to solve the interference problem of complex background when extracting target information; Second, aiming at the problem of direct fusion of conflicting information in different feature layers in the bi-directional path aggregation neck structure in the detection network, we propose an acrossscale feature pyramid with feature refinement structure to enhance the information characteristics of insulator defect targets and improve the multi-scale expression ability of the network. Then, aiming at the problem of difficult samples and imbalance of positive and negative samples in the process of target detection, we propose a focal shape intersection over union loss (focal-SIOU-loss), which improves the precision and stability of the regression process by introducing the weight adjustment mechanism of focal loss and the structural similarity of SIOU Loss. Finally, the experimental results show that, compared with the standard detection models such as YOLOv5, YOLO7, and YOLOv8, the proposed detection model achieves a better performance in the precision, recall rate, and robustness in detecting insulator defects under complex backgrounds.

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“…This is a one-stage detection method based on convolutional neural networks. Unlike traditional two-stage methods, such as R-CNN (regions with CNN features) and Fast R-CNN, YOLO adopts a single forward propagation approach, which completes the detection in a shorter time and achieves real-time performance [34,35]. Therefore, combining the YOLO algorithm with the chaotic system can achieve real-time monitoring and defect detection of dynamic harmonics scatter diagrams of cable grounding currents.…”

Section: Yolo (You Only Look Once) Is a Real-time Target Detection Al...mentioning

confidence: 99%

Defect Identification of XLPE Power Cable Using Harmonic Visualized Characteristics of Grounding Current

Wang,

Liu,

Huang

et al. 2024

Electronics

Self Cite

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This paper proposes an online monitoring and defect identification method for XLPE power cables using harmonic visualization of grounding currents. Four typical defects, including thermal aging, water ingress and dampness, insulation scratch, and excessive bending, were experimentally conducted. The AC grounding currents of the cable specimens with different defects were measured during operation. By using the chaotic synchronization system, the harmonic distortion was transformed into a 2D scatter diagram with distinctive characteristics. The relationship between the defect type and the diagram features was obtained. A YOLOv5 (you only look once v5) target recognition model was then established based on the dynamic harmonics scatter diagrams for cable defect classification and identification. The results indicated that the overall shape, distribution range, density degree, and typical lines formed by scatter aggregation can reflect the defect type effectively. The proposed method greatly reduces the difficulty of data analysis and enables rapid defect identification of XLPE power cables, which is useful for improving the reliability of the power system.

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High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

Cited by 18 publications

References 33 publications

Identification of weeds in fields based on computer vision technology

Identification of weeds in fields based on computer vision technology

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

Defect Identification of XLPE Power Cable Using Harmonic Visualized Characteristics of Grounding Current

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