Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure

Quincy G. Alexander, Vedhus Hoskere, Yasutaka Narazaki, Andrew Maxwell, Billie F. Spencer

AI in Civil Engineering 2022 article cited by 47

Source Open-access PDF More in Engineering & Materials

importance 5/10 ai assisted Infrastructure Maintenance and Monitoring infrastructure inspection stage: research

Problem

RGB-image-based automated damage detection in civil infrastructure degrades in poor and low-light conditions.

Why it matters

Missed cracks and defects in concrete infrastructure can compromise public safety.

What the AI did

A ResNet-based semantic segmentation model fuses thermal and RGB image features via dual encoders to automatically identify damage defects in concrete.

Results

not stated in source

Impact

Abstract proposes fusion to improve detection in changing lighting, but no measured outcomes are stated.

Record

Venue	AI in Civil Engineering
Published	2022
Work type	article
Authors	Quincy G. Alexander, Vedhus Hoskere, Yasutaka Narazaki, Andrew Maxwell, Billie F. Spencer
Citations	47 (OpenAlex, at import time)
References	27
Model or method	ResNet-based semantic segmentation model
Organization	not stated in source
Primary source	https://doi.org/10.1007/s43503-022-00002-y
Open access	diamond
Keywords	crack detection, thermal imaging, RGB fusion, semantic segmentation, civil infrastructure

Importance rating

5 / 10

real but narrower advance

Missed concrete damage affects public safety in civil infrastructure, but abstract gives no scale; a real but narrower methodological advance (RGB-thermal fusion) for inspection conditions.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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