Detection of Vehicle Body and Geometric Deviations Using Corrected Optical Measurements in Automotive Service Environments

<p style="margin-left:0cm; margin-right:0cm; text-align:justify">Reliable detection of vehicle body and geometric deviations in automotive service environments remains a challenging task due to unstable measurement conditions and heterogeneous surface properties. While optical diagnostic systems are widely used for non

Authors

Publication Details

Journal
European Journal of Technical and Natural Sciences
Published
2026-02-23
DOI
https://doi.org/10.29013/ejtns-26-1-22-24
Primary Topic
Surface Roughness and Optical Measurements
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection of Vehicle Body and Geometric Deviations Using Corrected Optical Measurements in Automotive Service Environments

E. G. Popov
European Journal of Technical and Natural Sciences
Surface Roughness and Optical Measurements
article

Detection of Vehicle Body and Geometric Deviations Using Corrected Optical Measurements in Automotive Service Environments

E. G. Popov
article en

Abstract

<p style="margin-left:0cm; margin-right:0cm; text-align:justify">Reliable detection of vehicle body and geometric deviations in automotive service environments remains a challenging task due to unstable measurement conditions and heterogeneous surface properties. While optical diagnostic systems are widely used for non

European Journal of Technical and Natural Sciences
Openalex Percentile: Top 43%
Surface Roughness and Optical Measurements
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.