State of play and future directions in industrial computer vision AI standards

2025-03-04

AI Summary

This paper addresses the lack of standardization for industrial-grade computer vision (CV) AI systems—particularly concerning reliability, explainability, data quality, security, and regulatory compliance. Through systematic literature analysis, multi-source standard comparison, policy text mining, and governance structure analysis, it surveys 67 active and in-development CV-related standards issued by ISO/IEC, IEEE, DIN, and other leading standardization bodies, identifying critical coverage gaps and cross-organizational coordination deficits. It presents the first comprehensive CV AI standardization landscape and proposes a three-stage evolutionary framework for trustworthy AI. The study yields an actionable, prioritized roadmap for industrial AI standardization, offering both theoretical foundations and practical guidance for global CV AI governance.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Audits of web technologies, standards, platforms, and applications

Abstract

The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of Computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor.

Problem

• Addressing reliability, transparency, and trustworthiness in AI models.
• Developing comprehensive industrial standards for computer vision AI.
• Analyzing current and future challenges in AI standardization efforts.

Innovation

• Develops standards for industrial computer vision AI
• Focuses on model interpretability and data quality
• Analyzes international CV standards and future directions