Enhancing the Performance of Computer Vision Systems in Industry: A Comparative Evaluation Between Data-Centric and Model-Centric Artificial Intelligence

This research contrasts model-centric (MCAI) and data-centric (DCAI) strategies in artificial intelligence, focusing specifically on optical quality control. It addresses the necessity for a thorough empirical study to evaluate both approaches under identical conditions. By examining casting and leather datasets, the study highlights that the quality and diversity of data play a more vital role in the success of models than merely fine-tuning hyperparameters. While MCAI delivers dependable results with superior datasets, DCAI methods—such as label correction, data augmentation, and generating synthetic data through diffusion models—significantly enhance recognition performance. For the casting dataset, accuracy increased from 83% to 93%, and for the leather dataset, from 53% to 62%. These results indicate that robust AI systems are built on high-quality, balanced data.