CURRENT LITERATURE REVIEW ON IMAGE PROCESSING ANALYSIS FOR CONCRETE DAMAGE ASSESMENT

Numerous studies have employed computer vision algorithms to analyze images of concrete damage. Therefore, conducting an image processing survey to detect concrete damage is very crucial. Thus, an image processing algorithm analysis survey to detect concrete damage was conducted using various algorithms and types of data from the last decade. The data observed were the first is damage to concrete, which included surface cracks, hairlines, crack width, patterns, holes, diagonal cracks, longitudinal cracks, and transverse cracks. The second part is figuring out where roads, bridges, and buildings are. The third is data sources like digital cameras, cameras built into phones, camera sensor systems, and unmanned aerial vehicles (UAVs). The study's findings indicate that image processing algorithms will play an essential role in future assessment research on the automation of concrete damage detection. This is particularly the case in high-risk regions for security reasons, and UAV technology is required to reach these locations.

Authors

Institutions

Publication Details

Journal
Jurnal PenSil
Published
2024-09-30
DOI
https://doi.org/10.21009/jpensil.v13i3.45042
Citations
2
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CURRENT LITERATURE REVIEW ON IMAGE PROCESSING ANALYSIS FOR CONCRETE DAMAGE ASSESMENT

Usman Wijaya, Yogi Yulianto, Emon Haryanto
2 citations
Jurnal PenSil
Infrastructure Maintenance and Monitoring
0.52
article

CURRENT LITERATURE REVIEW ON IMAGE PROCESSING ANALYSIS FOR CONCRETE DAMAGE ASSESMENT

Usman Wijaya, Yogi Yulianto, Emon Haryanto
article en
2 citations

Abstract

Numerous studies have employed computer vision algorithms to analyze images of concrete damage. Therefore, conducting an image processing survey to detect concrete damage is very crucial. Thus, an image processing algorithm analysis survey to detect concrete damage was conducted using various algorithms and types of data from the last decade. The data observed were the first is damage to concrete, which included surface cracks, hairlines, crack width, patterns, holes, diagonal cracks, longitudinal cracks, and transverse cracks. The second part is figuring out where roads, bridges, and buildings are. The third is data sources like digital cameras, cameras built into phones, camera sensor systems, and unmanned aerial vehicles (UAVs). The study's findings indicate that image processing algorithms will play an essential role in future assessment research on the automation of concrete damage detection. This is particularly the case in high-risk regions for security reasons, and UAV technology is required to reach these locations.

Jurnal PenSilVol. 13(3)
Universitas Trisakti (ID), Islamic University of Indonesia (ID), Janabadra University (ID)
Openalex Percentile: Top 36%
Infrastructure Maintenance and Monitoring
0.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.