Modern rail operations face the challenge of continuously improving efficiency, safety, and sustainability. In view of increasing transport volumes and complex logistical requirements, digitalisation is a decisive factor for competitiveness and future viability. For 20 years, Himmelsbach GmbH has been pioneering this field by developing and implementing RECOGNITIONPoints – innovative measurement systems for the comprehensive recording and analysis of passing freight wagons.
This technology enables customers with private sidings, such as ports, steelworks, refineries, industrial parks, and public rail operators, to gain detailed insights into their freight traffic. By capturing thousands of freight wagons from all perspectives with high-resolution 2D and 3D cameras, our RECOGNITIONPoints generate an immense amount of valuable data.
This whitepaper highlights the transformative potential of using a generic Large Language Model (LLM), trained with this extensive image material, to elevate the digitalisation of rail operations to a new level. Precise identification of wagons: The RECOGNITIONPoints recognise and read UIC wagon numbers, ensuring unique identification of each individual wagon. Furthermore, dangerous goods numbers (e.g., UN numbers) and accompanying routing sheets on the wagons are an important component to ensure safety and compliance with transport regulations.
The performance of RECOGNITIONPoints: data basis for intelligent analyses
The RECOGNITIONPoints from Himmelsbach GmbH form the foundation for comprehensive digital recording of freight traffic. These measurement systems are capable of obtaining detailed information from passing freight wagons, including:
- Comprehensive visual data: High-resolution 2D and 3D scans from all sides of the wagons precisely capture the current condition.
- Identification of wagon types: The analysis of axle patterns enables the automatic recognition of different freight wagon types.
- Geometric data: 3D scans provide exact information about dimensions and cargo such as containers.
- Detection of anomalies: Visual analyses can provide initial indications of damage, contamination, or improper loading.
The data collected over years in a wide variety of facilities and countries from millions of wagon recordings form a unique and valuable data treasure. This data treasure is the ideal basis for training a generic LLM specifically tailored to the needs and challenges of rail freight transport.
Generic Large Language Models (LLM) for rail operations: a paradigm shift
A generic LLM trained with the extensive image material opens up completely new possibilities for digitalisation and automation in rail operations. Unlike conventional image recognition systems, which are limited to identifying specific, predefined features, a trained LLM can develop a deeper and more context-related understanding of visual data.
Potential applications and advantages of such an LLM:
- Intelligent visual inspection: The LLM can learn to detect subtle anomalies, damage (e.g., rust, dents, cracks), incorrect loading, or missing components on freight wagons that would be difficult for conventional systems to identify.
- Detailed condition description: Beyond mere identification, the LLM can learn to describe the condition of a wagon in natural language (e.g., “The tank car shows strong signs of corrosion in the lower area of the tank wall”).
- Automated reporting: The LLM can automatically generate reports on the condition of the captured wagons, including detailed descriptions and potential needs for action.
- Improved data quality: Through intelligent analysis of visual data, errors in manual data entry can be reduced, and the overall quality of the captured information can be increased.
- Predictive maintenance: By analysing historical data and early detection of signs of wear, the LLM can contribute to the development of predictive maintenance strategies, minimise downtime, and extend the lifespan of the wagons.
- Optimisation of operational processes: The detailed information about the condition and nature of the wagons can contribute to the optimisation of shunting processes, route planning, and resource allocation.
- Support for classification and categorisation: The LLM can assist in the more precise classification of freight wagons and their cargo, which is relevant for logistical processes and compliance with regulations.
Himmelsbach GmbH has decades of experience in the development and implementation of innovative measurement systems for rail operations. Our RECOGNITIONPoints are in use in numerous countries and for a wide variety of industries, delivering reliable and precise data. This long-standing experience and the resulting unique database of millions of freight wagon recordings make us the ideal partner for the development and implementation of a generic LLM for rail operations. Our deep understanding of the specific requirements and challenges of our customers enables us to develop a tailor-made AI solution that offers real added value.
Outlook: The future of digitalisation in rail operations with LLMs
The integration of generic Large Language Models into the existing infrastructure of RECOGNITIONPoints holds enormous potential for the further development of digitalisation in rail operations. Through the ability to intelligently interpret visual data and convert it into usable information, we can help our customers:
- Increase efficiency: Through automation and optimised processes.
- Enhance safety: Through early detection of potential hazards.
- Reduce costs: Through improved maintenance and optimised procedures.
- Promote sustainability: Through a longer lifespan of the wagons and more efficient resource utilisation.
Himmelsbach GmbH is convinced that the use of generic LLMs represents a significant step towards intelligent and future-proof rail operations. We invite you to shape this exciting development together with us.