The study of binarization algorithm about digital rubbings image based on threshold segmentation and morphology

Wang, Cui; Zeng, Xuan; Yuan, Xiaocui

doi:10.3233/jcm-193812

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Thus, if the value is less than the image limit, it is set to 0, otherwise, it is set to the maximum value. As the objective of this work is to carry out the classification of the contamination of the insulator, this method is promising to define the boundaries of the images [38]. In the binarization process, the first argument is the source image, which is previously transformed into grayscale.…”

Section: Insulator's Datasetmentioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

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Section: Insulator's Datasetmentioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…At present, a large number of analytical and computational methods have been formed for image segmentation, such as segmentation techniques by setting thresholds, segmentation based on genetic algorithms, etc. Among them, the image segmentation based on the threshold method has the advantages of large applicability, high operational efficiency, and speed [46]. The calculation formula of the threshold method is:…”

Section: Concrete Surface Feature Recognitionmentioning

confidence: 99%

Evaluation of the Uniformity of Protective Coatings on Concrete Structure Surfaces Based on Cluster Analysis

Liu

Zhang

Tang

et al. 2021

Sensors

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With the continuous development of urbanization and industrialization in the world, concrete is widely used in various engineering constructions as an engineering material. However, the consequent problem of durability of concrete structures is also becoming increasingly prominent. As an important additional measure, a protective coating can effectively improve the durability of concrete performance. Moreover, the uniformity of the concrete surface coating will directly affect its protective effect. Therefore, we propose a nondestructive inspection and evaluation method of coating uniformity based on infrared imaging and cluster analysis for concrete surface coating uniformity detection and evaluation. Based on the obtained infrared images, a series of processing and analysis of the images were carried out using MATLAB software to obtain the characteristics of the infrared images of the concrete surface. Finally, by extracting the temperature distribution data of the pixel points on the concrete surface, an evaluation method of concrete surface coating uniformity based on a combination of cluster analysis and hierarchical analysis was established. The evaluation results show that the determination results obtained by this method are consistent with the actual situation. This study has a positive contribution to the testing of concrete surface coating uniformity and its evaluation.

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