Training cost analysis on a large computer vision defect detection model for ceramics

Within the sphere of industrial quality control, the adoption of computer vision techniques for defect detection in ceramics manufacturing represents a pivotal and long-overdue enhancement. Nonetheless, the economic viability of implementing such extensive models remains a concern for numerous small and medium-sized enterprises. This experimental study undertakes a detailed examination of the expenses related to the development of an expansive computer vision framework aimed at real-time defect detection in ceramics. Various facets of cost, encompassing computational requirements, temporal investments, and the financial outlay for data procurement, are scrutinized. The investigative approach melds quantitative data evaluation with predictive modeling to forecast the cost implications of model training. Moreover, a comparative analysis with conventional quality assurance methodologies is conducted to assess the financial and operational merits of the novel system. The discourse has the objective of furnishing insights into efficient resource distribution to optimize accuracy while minimizing fiscal output. The findings advocate for a cautious approach to the utilization of cloud-based computing resources in artificial intelligence endeavors, highlighting the computational capabilities of on-premise contemporary graphics processing units as a viable alternative. In scenarios where cloud-based GPU resources are unattainable, consumer GPUs emerge as a compromise, provided that legal impediments to their use in commercial applications can be navigated. Furthermore, despite the considerable initial outlay for model training, the prospective long-term advantages, such as improved detection precision and diminished reliance on manual inspection, may vindicate the investment. This research contributes to the ongoing dialogue regarding the financial practicability of integrating AI-enhanced quality control mechanisms within the ceramics industry, while providing comprehensive legal aspects.