Coverage-path-planned autonomous UAV inspection for deep learning-based corrosion grading of steel railway bridges
Year: 2026
Type: article
Abstract: Steel railway bridges require repeatable inspection methods capable of resolving spatially heterogeneous corrosion while reducing dependence on difficult-access surveys and continuous manual flight control. This study presents RustScan-DL/UAV, an AI-enabled framework coupling pilot-supervised autonomous UAV coverage-path planning with zone-level deep-learning corrosion grading. The planner generates geometrically calibrated boustrophedon paths, sequences bridge elements by a distance-minimising tour, and checks battery feasibility. Each image zone is graded using a fine-tuned ResNet-50 fused with hue-saturation-value oxidation scoring, producing five operational grades derived from ISO 4628-3:2024 and an exploratory photographic corrosion-severity proxy calibrated on a limited ultrasonic dataset. Validation used BRIDGE-760, a corrosion-grading benchmark of 760 images (12,160 zones) comprising three external corrosion datasets and a 106-image Chumphon steel railway bridge UAV campaign. RustScan-DL/UAV achieved 93.7% zone-level accuracy and F1=0.921 for critical-zone detection; HSV fusion improved accuracy by 2.5 percentage points over the CNN-only baseline. ResNet-50 is retained as the study-optimal backbone because it is the only candidate with complete BRIDGE-760, fusion, critical-zone, INT8 onboard, and closed-loop validation; lighter modern backbones are treated as prospective alternatives pending equivalent controlled tests. In 14 pilot-supervised flights, the planner achieved 98.4% predicted coverage and reduced flight distance by 31.2% relative to a naive sweep. The available flight logs support a 79% positive predictive value for triggered re-inspections but do not retain complete adjudication of non-triggered regions; consequently, a flight-level false-negative rate cannot be estimated from the archived campaign data and no safety-completeness claim is made. The resulting zone-level maps may support selection of locations for confirmatory inspection, while ultrasonic gauging remains necessary for structural assessment.
Source: Scientific Reports
Institution: Khon Kaen University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7204210189
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