Detection of Ice on Power Cables Based on Image Texture Features

Ice storms can cause major power disruptions and detection of ice formation on power cables can avoid them by taking preventive actions such as removing the ice before a major problem occurs. In this paper, a computer vision solution was developed to detect ice on difficult imaging scenarios where illumination and variety of locations can change the image of the cable and background considerably. The methodology starts with edge detection and support vector regression was applied to predict the upper threshold for the Canny edge detector. We found that this improves the later classification accuracy. A total of 44 image features based on the gray level co-occurrence matrix, statistical features and Hough transform were extracted and then processed by principal component analysis and sequential forward selection to reduce the feature dimension. Final detection was performed using four different classifiers. It was found that eight features achieved optimal performance using a support vector machine.