Towards Workflows for the Use of AI Foundation Models in Visual Inspection Applications

Abstract

The latest successes in AI have been largely driven by a paradigm known as Foundation Models (FMs), large Neural Networks pretrained on massive datasets that thereby acquire impressive transfer learning capabilities to adapt to new tasks. The emerging properties of FMs have unlocked novel tantalizing applications for instance enabling the generation of fluent text and realistic images from text descriptions. The impact of FMs on technical domains like civil engineering is however still in its infancy, owing to a gap between research development and application use cases. This paper aims to help bridge this gap and promote adoption among technical practitioners, specifically in visual inspection applications for civil engineering. For that we analyze the requirements in terms of data availability making particular use cases amenable to the pretraining/fine-tuning paradigm of FMs, i.e. situations where labeled data is scarce or costly, but unlabeled data is abundant. We then illustrate proof-of-concepts workflows using FMs, in visual inspection applications. We hope that our contribution will mark the start of conversations between AI researchers and civil engineers on the potential of FMs to accelerate workflows supporting vision tasks for maintenance inspections and decisions.