Automated bridge coating defect recognition using adaptive ellipse approach

Abstract

Image processing has been used for assessment of infrastructure surface coating conditions for years. In North America, civil engineers have utilized image recognition for steel bridge coating inspection since late 1990s. However, there is still no robust method to overcome the non-uniform illumination problem for infrastructure surface coating defect recognition to date. Therefore, this paper aims to develop a new approach to tackle the non-uniform illumination problem for rust image recognition. This paper starts with an investigation of 14 color spaces in order to find out the best color configuration for non-uniformly illuminated rust image segmentation. Then, the identified best color configuration a⁎b⁎, which has a moderate ability to filter light, is utilized to develop the proposed adaptive ellipse approach (AEA). In AEA, a rust image is partitioned into three parts: background, rust, and mild-rust-color spots. The main idea is to identify the mild-rust-color spots properly using an adaptive ellipse. Illumination adjustment is also adopted in this approach to overcome the non-uniform illumination problem. Finally, the performance of the AEA-based a⁎b⁎ configuration is compared to the K-Means method, one of the most popular and effective image recognition approaches, to show the effectiveness of the proposed AEA approach.