Damage detection with image processing: a comparative study

Large structures, such as bridges, highways, etc., need to be inspected to evaluate their actual physical and functional condition, to predict future conditions, and to help decision makers allocating maintenance and rehabilitation resources. The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations. Traditional techniques in structural health monitoring (SHM) involve visual inspection related to inspection standards that can be time-consuming data collection, expensive, labor intensive, and dangerous. To address these limitations, machine vision-based inspection procedures have increasingly been investigated within the research community. In this context, this paper proposes and compares four different computer vision procedures to identify damage by image processing: Otsu method thresholding, Markov random fields segmentation, RGB color detection technique, and K-means clustering algorithm. The first method is based on segmentation by thresholding that returns a binary image from a grayscale image. The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels. The RGB technique uses color detection to evaluate the defect extensions. Finally, K-means algorithm is based on Euclidean distance for clustering of the images. The benefits and limitations of each technique are discussed, and the challenges of using the techniques are highlighted. To show the effectiveness of the described techniques in damage detection of civil infrastructures, a case study is presented. Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.