A Two-Level Semantic Segmentation Framework for Visual Corrosion Pattern Assessment in Steel Bridges

Abstract

Corrosion assessment in steel bridges requires not only estimating the spatial extent of visible deterioration but also distinguishing visual corrosion appearances that may support inspection interpretation. Existing vision-based corrosion-segmentation studies predominantly formulate the task as binary corrosion-versus-background mapping, which supports damage localization but provides limited information regarding corrosion-pattern distribution. This study proposes a two-level semantic segmentation framework for visual corrosion assessment of steel bridge components. Level 1 performs binary pixel-wise segmentation to quantify visible corrosion coverage, whereas Level 2 assigns corrosion pixels to four inspection-oriented visual categories: Uniform, Crevice, Underfilm, and Other Localized corrosion. Field images were annotated using an operational visual taxonomy by the first author, and category-label reproducibility was subsequently evaluated through a blinded second-annotator audit of the categorized validation polygons. Transformer-based and CNN-based models were evaluated using common image subsets, input resolution, and evaluation metrics, while model-specific implementation and optimization settings were documented explicitly. Performance was assessed using region-overlap metrics, class-wise precision, recall, and F1-score, and Boundary-F1 to quantify edge agreement along irregular corrosion fronts. The highest-performing binary configuration achieved an mIoU of 0.843. The adopted formula-derived Level 2 configuration achieved a five-class mIoU of 0.605 and a mean corrosion-category F1-score of 0.678; a manual-weighting variant produced a marginally higher five-class mIoU of 0.607 but was not adopted as the primary formula-derived SegFormer configuration. Binary-merged evaluation showed that Level 2 retained substantial corrosion-coverage capability, although dedicated binary models remained superior for foreground–background segmentation. The framework provides complementary representations of corrosion coverage, visual-category distribution, and boundary quality. It is intended to support, rather than replace, expert inspection, field measurement, and engineering evaluation.