Non-destructive detection and quantification of corrosion damage in coated steel components with different illumination conditions

Abstract

Existing deep learning-based detection methods for corrosion damage in steel structures are mostly applicable under normal lighting conditions and lack an association between detection results and damage levels. Focuses on coated corrosion steel components under various illumination conditions, this paper presents a YOLOv8s-G network tailored for pixel-level image segmentation and quantification of corrosion damage. A dataset of 1299 images of corroded steel components with different illumination conditions was captured in a field steel structure workshop. Furthermore, the ability of network to extract multi-scale corrosion features across various illumination conditions was enhanced by integrating the C2f-S module and fusion splicing method. The advancement and generalization of YOLOv8s-G were verified through comparisons with other state-of-the-art networks and tests on public datasets. Finally, the ratio of the corrosion area to the cross-sectional area of the steel component was calculated using morphological image operations, quantifying the relative area occupied by corrosion. The accuracy of this quantification method was further validated through comparison with filed measurements. Our research can enhance the reliability of decision-making regarding steel structural corrosion damage.