All over the world, countries and local authorities are obliged to expand, maintain and monitor the road and rail network. These tasks must be fulfilled reliably and economically. Automated, intelligent systems can help to make the ever-increasing scope of construction and monitoring measures of a constantly growing traffic network transparent and controllable.
With AI-based image processing, Data Spree makes it possible to fully monitor rail networks and roadways under the most diverse conditions and weather patterns. Various construction measures, inspections and maintenance work on rails and roads can also be qualitatively reliably assured or fully automated.
Today, roads, tracks and motorways often require versatile monitoring to reliably detect and classify vehicles, license plates, vehicle movements, vehicle distances, lane changes, congestion, people or foreign objects on the road. AI-based image processing enables precise detection and classification of various scenarios under different light and weather conditions. Even with borderline cases, difficult conditions or complex traffic situations, AI-based systems with appropriately trained neural networks can safely deal with them. Rule-based image processing solutions are often not up to versatile and unpredictable situations and are prone to errors. With AI-based image processing, accidents and traffic jams become predictable and can be prevented before they occur. Artificial intelligence will make the roads and highways of the future more efficient and safer.
The laying and maintenance of rail infrastructure is time-consuming and costly. With AI-based image processing it is possible to assist and fully automate many inspection and quality assurance processes. For example, various defect images on the rail, track bed or cable layer during construction or maintenance can be detected and classified in real time. These possibilities can of course also be used for driver assistance systems or the detection of vegetation damage or train signals. Due to the different light and weather conditions and the high variance of error images, conventional, rule-based image processing systems cannot detect or correctly interpret many situations. AI-based image processing learns scenarios on the basis of real data and real scenarios and thus offers a reliable solution for every situation.
Road and rail inspection systems help to take necessary maintenance and repair measures in good time. In the past, these time-consuming and labour-intensive measures had to be carried out manually by employees. Using AI-based image processing systems, cracks or other damage in road surfaces, rail networks or tunnels can be automatically detected and correctly interpreted under any light, weather and environmental conditions. These inspections can be carried out independently and on a long-term basis without having to interrupt or restrict traffic on roads and railways. Artificial intelligence thus opens up many new possibilities for dealing with this enormous maintenance and repair effort more effectively and cost-efficiently in the future.