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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20101 - Civil engineering

Project

<a href="/en/project/TM04000012" target="_blank" >TM04000012: A concrete bridge health interpretation system based on mutual boost of big data and physical mechanism</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Name of the periodical

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Volume of the periodical

Part B

Issue of the periodical within the volume

158

Country of publishing house

GB - UNITED KINGDOM

Number of pages

29

Pages from-to

1-29

UT code for WoS article

001519877700004

EID of the result in the Scopus database

2-s2.0-105008792709