Optimizing deep learning-driven computer vision for civil infrastructure defect Identification: Challenges and strategies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0200271" target="_blank" >RIV/00216305:26110/26:0200271 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0952197625015234?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197625015234?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2025.111521" target="_blank" >10.1016/j.engappai.2025.111521</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing deep learning-driven computer vision for civil infrastructure defect Identification: Challenges and strategies

Popis výsledku v původním jazyce

With advancements in Internet of Things (IoT) technologies and deep learning, Structural Health Monitoring (SHM) is progressing towards long-distance and intelligent applications. To promote the widespread adoption of deep learning in vision-based SHM, this survey compiles optimization strategies derived from deep learning models specifically designed for defect identification in civil infrastructure-an area that has not been comprehensively explored. First, a concise overview of fundamental deep learning models for vision-based defect identification is provided. Next, optimization methods are categorized into three main groups, each addressing a distinct challenge encountered in practical vision-based SHM: optimizing considering defect-specific characteristics, lightweight design for real-time identification, and enhance robustness under complex environmental conditions. These methods are further classified systematically based on their similar features. This survey offers researchers a deeper understanding of the challenges in vision-based defect identification and assists them in selecting appropriate optimization techniques to address these challenges. Ultimately, it aims to enhance the effective deployment of deep learning models in vision-based SHM by improving accuracy, enabling real-time operation, and facilitating automated defect identification.

Název v anglickém jazyce

Optimizing deep learning-driven computer vision for civil infrastructure defect Identification: Challenges and strategies

Popis výsledku anglicky

With advancements in Internet of Things (IoT) technologies and deep learning, Structural Health Monitoring (SHM) is progressing towards long-distance and intelligent applications. To promote the widespread adoption of deep learning in vision-based SHM, this survey compiles optimization strategies derived from deep learning models specifically designed for defect identification in civil infrastructure-an area that has not been comprehensively explored. First, a concise overview of fundamental deep learning models for vision-based defect identification is provided. Next, optimization methods are categorized into three main groups, each addressing a distinct challenge encountered in practical vision-based SHM: optimizing considering defect-specific characteristics, lightweight design for real-time identification, and enhance robustness under complex environmental conditions. These methods are further classified systematically based on their similar features. This survey offers researchers a deeper understanding of the challenges in vision-based defect identification and assists them in selecting appropriate optimization techniques to address these challenges. Ultimately, it aims to enhance the effective deployment of deep learning models in vision-based SHM by improving accuracy, enabling real-time operation, and facilitating automated defect identification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/TM04000012" target="_blank" >TM04000012: Systém pro zjišťování stavu betonových mostů založený na na vzájemné podpoře velkých dat a mechaniky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

Part B

Číslo periodika v rámci svazku

158

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

29

Strana od-do

1-29

Kód UT WoS článku

001519877700004

EID výsledku v databázi Scopus

2-s2.0-105008792709