Study on the Calculation Method of Ice Thickness Calculation and Wire Extraction Based on Infrared Image

Chang, Yadong; Yu, Hong; Kong, Liuyang

doi:10.1109/icma.2018.8484471

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the weather changes drastically, the method may not perform well. Chang (2018) proposed a study on the calculation of ice thickness and wire extraction based on infrared images. The method uses infrared images as the input for ice thickness detection.…”

Section: Related Workmentioning

confidence: 99%

Rotation invariant angle-density based features for an ice image classification system

Yue

Yuan

Lü

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

One of the natural disasters which cause economic loss and are a serious threat to society are ice covering phenomena for overhead transmission. This paper presents a new method for ice and non-ice image classification to improve ice detection results. The proposed method explores wavelet decomposition to extract robust features, such as those invariant to rotation, scaling and thickness of ice for classification. The proposed method estimates the average of the high frequency sub-bands for each level. Then it obtains Canny edge components for the average wavelet image at each level. The proposed method studies shapes of edge components to identify the presence of ice. To achieve this, the proposed method finds the major and minor axes for each edge component, and then draws parallel lines to the major and minor axes over the edge components. For each parallel line to the major and minor axes, the proposed method further extracts angle and density-based features for pixels that fall on the parallel lines to the major and minor axes. Next, the proposed method selects features from each average wavelet image and further calculates the mean for the feature vectors corresponding to the level, which results in a feature matrix. Finally, the feature matrix is fed to a Multi-Layer Perceptron Neural Network for classifying ice and non-ice images. Experimental results on a diversified dataset and comparative study with an existing method show that the proposed method is useful for accurate ice detection with better accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%