Automatic corrosion estimation using galvanized steel images on power transmission towers

We propose a method that automatically estimates the degree of corrosion of galvanized steel of power transmission towers using digital images of the steel. Electric power companies have to determine the corrosion degree of steel for the maintenance of the towers. Accordingly, the technique to estimate the degradation degree of galvanization objectively and nondestructively is need. Our method is based on a support vector machine using the radial basis function kernel. Moreover, we define a new technique that adds artificially processed images to the training data, to handle the variations in color using automatic white balance and exposure of a digital camera. We evaluated our method using 1,427 images of 8 towers. Our method achieved an average precision of 85.6% using a hue-saturation-value histogram feature vector.