Corrosion progression evaluation on steel bridges using deep learning and onboard inspection images

Abstract

This study aims to develop a system for visualizing and quantitatively evaluating corrosion progression on steel bridges. First, images taken in different years are geometrically aligned using ECC-based alignment, followed by dividing panoramic images into an 2×4 grid (referred to as gridization in this study). YOLO11 is then applied to each grid to detect corrosion regions with high accuracy and speed. Next, corrosion progression is visualized in red using the additive mixture of color method by comparing aligned images. Subsequently, image processing techniques such as histogram equalization, HSV color space conversion, and binarization are applied to compute corrosion progression at the pixel level. Through demonstration experiments using actual inspection images, the system was able to extract progression areas and perform quantitative evaluations for a portion of the image set. In addition, due to overlapping detections caused by a low detection threshold for minimizing missed detections, the corrosion progression is aggregated as an average corrosion progression value to mitigate overcounting. The proposed system contributes to reducing the workload of inspection engineers, standardizing evaluation results, and enhancing the efficiency of large-scale structural maintenance. It also offers potential for further development as a practical tool in visual inspection processes by improving the accuracy of image alignment, refining color extraction thresholds, and incorporating grid-level evaluation across broader datasets.