My New Publication and Certificate

I am pleased to share that my article “Detection of Vehicle Body and Geometric Deviations Using Corrected Optical Measurements in Automotive Service Environments” has been published in the European Journal of Technical and Natural Sciences, №1, 2026 (ISSN 2414–2352).

The journal is indexed in international databases, and I have received an official certificate from Premier Publishing confirming this publication.

How Corrected Optical Measurements Improve Vehicle Body Deviation Detection

New research published in the European Journal of Technical and Natural Sciences

Topics:

Automotive Diagnostics Computer Vision Optical Metrology Measurement Science Vehicle Inspection

What the Article Is About

Reliable detection of vehicle body geometry deviations is critical for safety, aerodynamic performance, and structural integrity. Optical diagnostic systems are widely used for non-contact inspection, but in real service environments — with variable lighting, sensor repositioning, and heterogeneous surfaces — measurement instability often masks small but significant defects.

This article presents methods for detecting geometric deviations using corrected optical measurements obtained after adaptive geometric and photometric correction. The proposed approach operates on stabilized, uncertainty-aware data and focuses on identifying inconsistencies in body panels and aerodynamic components, including vehicles with modified or non-standard geometry.

Key Results

Experimental evaluation under real automotive service conditions (200–1500 lux ambient light) showed:

· False-positive deviation detection substantially reduced by filtering ghost geometries caused by specular reflections

· Repeatability improved threefold — standard deviation of geometric residuals reduced compared to raw optical data

· Resolution down to 0.35 mm on high-gloss surfaces, whereas uncorrected measurements failed below 1.2 mm

“Adaptive correction does not merely improve measurement quality. It fundamentally enables consistent geometric diagnostics in non-structured service environments.”

Why This Matters for Automotive Diagnostics

When optical measurements are unstable, diagnostically significant deviations may be masked by noise or falsely detected. By incorporating uncertainty covariance into the deviation model, the system bridges the gap between raw computer vision data and metrological standards.

The proposed methods provide a practical basis for subsequent feature-level analysis and decision-support systems within automotive cyber-physical architectures.

Certificate of Publication

The article was published in the European Journal of Technical and Natural Sciences (№1, 2026), a peer-reviewed journal with Index Copernicus Value (ICV) 92.08 for 2022. The certificate, issued by Premier Publishing on February 28, 2026, confirms the official publication.

Read the Full Article

The journal is available through Premier Publishing’s open access repository. For more details, please visit:

🔗 ppublishing.org

Author:

Evgeny Popov, PhD — Developer of Automotive Digital Diagnostic Systems. Author of engineering books and scientific publications on optical diagnostics, computer vision, digital twins, predictive analytics, and digital training for automotive service operations.