Appropriate preprocessing with brightness control for corrosion detection of Steel bridges using Convolutional Neural Networks

Abstract

It is important to need to be improve the efficiency of inspection as bridges are aging in recent years. This study developed the learning model which applied convolutional neural network to our corrosion detector using training data based on photographs of road bridge inspection results in Fukushima Prefecture. We integrated the training data from road offices in two areas and applied pre-processing with focusing on the brightness change of the images before training. As a result of the validation using the test data based on the field survey results, we could find that different pre-processing methods were suitable in accordance with a target. The accuracies of corrosion, paint deformation, and concrete classes was improved by the the learning models using the training data with contrast reduction, histogram flattening and contrast enhancement, respectively. Thus, the classification accuracy can be maximized by developing a learned model using training data applied appropriate pre-processing with brightness control.