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

arXiv: https://arxiv.org/abs/2503.02675 · HTML: https://mlsanity.com/paper/2503.02675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://mlsanity.com/paper/2304.05170/) (2023)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning](https://mlsanity.com/paper/2012.08174/) (2020)
- [Smart Traffic Monitoring System using Computer Vision and Edge Computing](https://mlsanity.com/paper/2109.03141/) (2021)
- [User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments](https://mlsanity.com/paper/2103.16434/) (2021)
- [Key Safety Design Overview in AI-driven Autonomous Vehicles](https://mlsanity.com/paper/2412.08862/) (2024)

## Abstract (arXiv)

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.