About the project
The job of a freight train inspector is to physically walk around every wagon of a train and check, if there are any broken or used components that might cause a risk or need to be maintained. This new software transfers ultra high resolution images of freight wagons directly from the field into the office and allows a remote inspection. The inspection process is further supported by artificial intelligence, that analyses all images and visually highlights any components that are not within the tolerance range.
© Roland Fischer, Zürich (Switzerland)
The general concept is designed for two screens:
One is used for meta information, such as wagon identifiers, dimensions or annotated components.
One is used for meta information, such as wagon identifiers, dimensions or annotated components.
The second screen displays images, tools for image analysis and components which have been annotated by the machine learning model (artificial intelligence). This screen is vertically oriented with three images above each other. This reflects the position of the three different cameras, that are vertically stacked in the real world. This allows the inspectors to process all three images simultaneously:
The content of both screens is synced. This way we make sure, that the user always sees the contextually relevant information.
Our efforts
From the very beginning of this project, we had the possibility to work closely with the train inspectors. Together we created and discussed the general concept of the software as well as the detailed interaction patterns. We followed a user centered design approach including workshops with various key users, and thorough user testings. Starting with first sketches, we then transferred these ideas to a visually appealing user interface, based on the SBB web design guidelines, which we needed to adapt and extend for the specific use within desktop applications.
