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

Year 2025, Volume: 11 Issue: 3, 426 - 448, 31.12.2025

Berk Ercoşkun

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

Image Processing , Artificial Intelligence , Quality Control , Industrial Automation , Deep Learning , Computer Vision in Manufacturing

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