Detection of rust defects on steel bridge coatings via digital image recognition

Abstract

Abstract One of the major expenses for steel structures is the anti-corrosion maintenance tasks. The maintenance of a steel structure depends on regular inspections, and visual inspections are often adopted in Taiwan. Using the naked eye to determine the rusted area percentage greatly depends on the experience of the inspector, resulting in subjective results. As an alternative, an algorithm consisting of three different approaches is proposed to automatically process images. The Hue percentage and coefficient of variation (COV) of the gray levels are used to divide images into three groups in which each group is assessed using a specific recognition technique. The three proposed techniques are the following: the traditional K-means method in the H component, the double-center-double-radius (DCDR) algorithm in the Red-Green-Blue (RGB) color space and DCDR in the Hue-Saturation-Intensity (HSI) color space. Additionally, the Least Square Support Vector Machine (LS-SVM) was adopted to predict the radii in the DCDR approaches. One hundred images, mostly collected outdoors, were used to verify the proposed algorithm. Promising performance was observed, particularly for images with non-uniform illumination.