A Hybrid YOLO and Segment Anything Model Pipeline for Multi-Damage Segmentation in UAV Inspection Imagery
Rafael Cabral; Ricardo Santos; José A.F.O. Correia; Diogo Ribeiro · 2025 · Sensors
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evalua
Abstract by Rafael Cabral; Ricardo Santos; José A.F.O. Correia; Diogo Ribeiro, Sensors (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/s25216568