RailScan
Railscan is an AI-based technology designed to automatically analyze high-resolution images of railway tracks acquired by inspection and monitoring systems.
By combining computer vision and machine learning techniques, the system automatically identifies track components and detects potential defects directly from the acquired imagery.
The technology can be trained and upgraded to recognize specific elements or defect categories, allowing the analysis to adapt to different railway infrastructures, inspection systems and operational requirements.
Automatic Defects Recognition
Integrated artificial intelligence algorithms automatically identify structural features and anomalies in track images.
These include cracks and surface defects, presence and alignment of track components, deformation patterns, wear indicators and other elements relevant to infrastructure condition assessment.
RailScan supports operators in the analysis of large volumes of inspection imagery by rapidly scanning railway track elements and highlighting relevant information.
The system can process datasets acquired during inspection campaigns, enabling efficient analysis either on-board inspection platforms or remotely.
The technology is fully compatible with ADTS railway track acquisition systems.
