Controlled Benchmarking and Component-Aware Ablation for Railway Viaduct Structural and Damage Segmentation

Abstract

Automated damage inspection of railway viaducts requires pixel-level identification of structural components and surface damage such as cracking and rebar exposure. A common assumption in bridge inspection is that damage segmentation improves when component information is provided alongside the image. This study tests that assumption on the Tokaido synthetic viaduct dataset using controlled comparisons between segmentation models with and without component information. Both damage and structural component segmentation are evaluated across multiple architectures, and the trained component model is assessed on real viaduct photographs against a baseline model requiring no task-specific training. Under the original random split, explicit component conditioning does not produce a measurable improvement in damage segmentation: all tested strategies remain within 0.008 mean Intersection-over-Union (mIoU) of a baseline without component input, and this null result persists even when component predictions are reliable. Under a leakage-controlled scene-disjoint split, however, the same component-aware variants show a small positive trend (up to +0.019 mIoU over three seeds), so the effect of component conditioning depends on the evaluation protocol. The best unconditioned model reaches 0.569 mIoU for damage segmentation; for real-photo component segmentation, the trained model reaches 0.424 mIoU compared with 0.250 mIoU for the training-free baseline. These results show that multitask benefits reported in bridge inspection do not automatically translate into gains from explicit use of component information on synthetic viaduct data, where damage occurs almost exclusively on columns yet is too sparse for structural element identity to yield more than a marginal localisation gain. The multi-architecture benchmark and the measured real-photo structural transfer gap provide reference baselines for subsequent work on component-aware and transfer-robust inspection.