Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives

Berk Ercoşkun

Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives

Abstract

Guided by PRISMA, this review synthesizes research from 2000 to 2025 on image processing and AI-driven quality control in industry, placing feasibility ahead of accuracy as the primary criterion. It links algorithmic choices with optics, lighting, and automation so that system design aligns with cycle time, robustness targets, available data regimes, explainability needs, and the cost of quality. Departing from earlier surveys centered on accuracy or a single method family, the review co-evaluates hardware and algorithms alongside online latency, environmental variability, data scarcity and class imbalance, operator acceptance, and economic payback. Comparative analyses of Convolutional Neural Networks, autoencoders, and Vision Transformers consider data demands, computational cost, throughput, reliability, and interpretability, and culminate in an implementation playbook covering transfer learning, synthetic data generation, calibration and contamination control, and human-in-the-loop workflows tied to line constraints. Outcomes are interpreted through an ROI framework that connects inspection design to scrap, rework, appraisal effort, and payback windows. The review closes with a forward agenda on edge inference, federated learning, and multimodal sensing, together with governance, cybersecurity, and workforce upskilling. Overall, it offers a concise, decision-oriented guide to selecting, deploying, and sustaining AI-driven inspection on real production lines.

Keywords

References

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Details

Primary Language

English

Subjects

Mechanical Engineering (Other)

Journal Section

Review

Authors

Berk Ercoşkun ^*
0009-0003-5627-334X
Türkiye

Publication Date

December 31, 2025

Submission Date

June 25, 2025

Acceptance Date

November 7, 2025

Published in Issue

Year 2025 Volume: 11 Number: 3

IZ

https://izlik.org/JA23AJ98BB

Cite

RIS / Bibtex

APA

Ercoşkun, B. (2025). Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives. Gazi Journal of Engineering Sciences, 11(3), 426-448. https://izlik.org/JA23AJ98BB

AMA

1.Ercoşkun B. Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives. GJES. 2025;11(3):426-448. https://izlik.org/JA23AJ98BB

Chicago

Ercoşkun, Berk. 2025. “Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives”. Gazi Journal of Engineering Sciences 11 (3): 426-48. https://izlik.org/JA23AJ98BB.

EndNote

Ercoşkun B (December 1, 2025) Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives. Gazi Journal of Engineering Sciences 11 3 426–448.

IEEE

[1]B. Ercoşkun, “Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives”, GJES, vol. 11, no. 3, pp. 426–448, Dec. 2025, [Online]. Available: https://izlik.org/JA23AJ98BB

ISNAD

Ercoşkun, Berk. “Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives”. Gazi Journal of Engineering Sciences 11/3 (December 1, 2025): 426-448. https://izlik.org/JA23AJ98BB.

JAMA

1.Ercoşkun B. Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives. GJES. 2025;11:426–448.

MLA

Ercoşkun, Berk. “Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives”. Gazi Journal of Engineering Sciences, vol. 11, no. 3, Dec. 2025, pp. 426-48, https://izlik.org/JA23AJ98BB.

Vancouver

1.Berk Ercoşkun. Integration of Image Processing Techniques into Automotive Quality Control Processes: Methods, Applications, and Future Perspectives. GJES [Internet]. 2025 Dec. 1;11(3):426-48. Available from: https://izlik.org/JA23AJ98BB