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AI-supported object recognition for a modern road infrastructure

RAG
Large Language Models (LLM)
Customer:Federal Ministry of Transport and Digital Infrastructure
Industry:Public administration

P&M developed an AI platform for the automated detection and assessment of road signs and road damage for the Federal Ministry of Transport and Digital Infrastructure. The system identifies and prioritizes maintenance tasks, facilitates the work of road maintenance departments and ensures greater efficiency, quality and safety in road management.

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Automated object recognition

The system uses neural networks to automatically identify road signs, damage and soiling. This has reduced manual checks by over 60% - while at the same time increasing detection accuracy.

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Secure data processing

All analysis and storage processes are GDPR-compliant and take place in a secure cloud environment. Data protection impact assessments (DPIA) guarantee the highest standards of data security and governance.

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Intuitive visualization analysis

A user-friendly web interface visualizes the results clearly and concisely. Damage, maintenance requirements and priorities can be tracked intuitively and processed in a targeted manner.

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Challenges

Manual processes, high data volumes, lack of transparency

Before the start of the project, road signs were checked completely manually - a time-consuming and error-prone process. The aim was to find an intelligent solution that would automate these processes, ensure data quality and speed up decision-making.

  • Manual inspections with high time expenditure
  • Lack of prioritization of maintenance measures
  • Incomplete or error-prone data entries
  • Need for scalable, secure data infrastructure
Implementation

Agile development and AI integration

P&M implemented the project in agile iterations - from the requirements analysis and training of the AI models to the provision of the productive platform. The solution combines precise object recognition with intuitive operation and secure cloud architecture.

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Analysis & concept phase

Joint workshops with the ministry's specialist departments defined requirements, data sources and security guidelines. The technical concept was created on this basis.

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Model development & training

TensorFlow was used to train AI models for object recognition and damage classification. Specially curated image data sets and depth detection algorithms were used.

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Integration & front-end development

Angular was used to create a user-friendly web application that visualizes AI results in real time and automates data entry.

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Backend & cloud architecture

Back-end integration in C# enables scalable processing of large volumes of data. Cloud services ensure secure storage, analysis and provision of results.

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Testing under real conditions

To ensure maximum reliability, the system was tested under various weather conditions such as rain, snow and darkness and iteratively improved.

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Optimization & quality assurance

The final platform was tested according to ISO and GDPR standards and released for productive use - with continuous performance monitoring and feedback loops.

Result

Efficiency, precision and data security in road management

The new AI platform has enabled the BMVI to fundamentally modernize its inspection processes. Automated detection, secure data processing and clear visualization lead to shorter response times, higher data quality and efficient use of resources.


  • Reduction of manual processes by 60%

  • GDPR-compliant cloud architecture with high data security

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