Leveraging Commercial AI/ML for Autonomous NDI's

For Phase I we used commercially available hardware and software to develop a computer vision AI/ML model that detects corrosion in aerial images of vessels. This required robust cloud hosting and cloud platform/applied machine learning capabilities; a fully automated data pipeline; and extensive collaboration with our drone flight services partner to obtain high quality images. To increase the business value to the Navy and commercial viability, we plan to turn the model into a platform solution in Phase II. Our goal for Phase II is to create a data fusion platform of which the existing automated data pipeline and corrosion detection model are pieces. Making this a standalone platform requires a front-end user interface (UI) and enhanced integration and modeling capabilities. The UI will turn data analyses into actionable business insights provided directly to the inspectors/business users in the field. Without a UI the model cannot “serve up” the results of the analyses and business users have no way to engage with the model or the findings. We will also fuse other sensor data with the RGB images to account for “invisible” corrosion undetectable optically. The fusion of these data sources/analyses will predict maintenance and repair needs. Based on Phase I lessons learned and MRO market research, our technical objectives for Phase II are: Enhance data capture using autonomous drones.  Develop a multi-label classification algorithm.  Develop end user interface and enhanced 3D modeling capabilities. Provide CONOPS for integration with existing maintenance and scheduling systems. Investigate the use of other sensors (eg. infrared, acoustic, electrochemical) and submersibles to identify "invisible" corrosion. Field test the solution.