Ecosystem - Added value
In this article, we describe how the AM-RAIL™ can be implemented to our clients' ecosystems. The article includes a description of the implementation process and explains the added value to a railway operations. The goal of this article is to give an idea of how AM-RAIL™ can be implemented to our clients' ecosystem, as we aim to gain pilot projects for AM-RAIL™.
Implementing AM-RAIL™ does not require expertise of AI-solutions from the client. Only data from the client's ecosystem is required. This data can be gathered from existing or newly installed sensors (Cameras, LiDars, Sonars). The data gathered from the field needs to be related to a CAD drawing of the components. These CAD drawings can also be used for recognition operations by rendering the drawings and relating them to the sensor data.
Phase 1 - Data acquisition. The client provides the CAD drawings, video and LiDar cloud data from the railway network. This can be done by using existing data or by gathering data with a new hardware set-up.
Phase 2 - Data annotation. Recon AI labels the video and LiDar data and renders the CAD drawings to annotate the data for the training phase.
Phase 3 - Training. Recon AI plans, builds and tests neural network models for training the AI to gain sufficient recognition performance.
Phase 4 - Deployment. Recon AI deploys the trained AI algorithms to the on-site sensors via API.
Phase 5 - Development. Recon AI further develops the solution in order to recognise additional components of interest and to improve recognition performance and measurement accuracy.
We estimate the first implementation circle to take 6-12 months.
Ecosystem - Added Value
Immediate added value of the AM-RAIL™ is the automation of measurements performed currently in installation operations. In addition to this, point-cloud information of components can be utilized for object detection, 3D modelling of infrastructure (asset management) and combining information of infrastructural changes for predictive maintenance operations.