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Neuroinformatics and
Cognitive Robotics Lab

headerphoto Neuroinformatics and 
Cognitive Robotics Lab
Contact Person

Univ.-Prof. Dr.-Ing. Horst-Michael Groß

Head of department

Phone +49 3677 692858

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Title:ASINVOS - Assistierendes und Interaktiv lernfähiges Videoinspektionssystem für Oberflächenstrukturen am Beispiel von Straßenbelägen und Rohrleitungen
Duration:01.01.2016 - 30.09.2018
Funding:BMBF - Bundesministerium für Bildung und Forschung
Project Partner:

Bundesanstalt für Straßenwesen
Lehmann + Partner GmbH, Erfurt
OPTIMESS Engineering GmbH, Gera
Thüringer Fernwasserversorgung

Project Manager:Prof. Dr. Horst-Michael Groß

Dipl.-Inf. Markus Eisenbach
Dipl.-Inf. Ronny Stricker

Preceding Projects:RODIAR


Most public infrastructures in urban areas are aging and need frequent inspection. Distress detection and a solid management for maintenance is the key to guarantee their permanent availability. Current evaluation methods are mostly focused on visual inspection which is done manually and therefore requires excessive manual labor. Therefore, the time between the actual inspection and the final evaluation may take up to several months. In the meantime small damages like cracks can lead to substantial downtimes with a high impact for the population.

The aim of the ASINVOS project is to automate this process to a high degree by applying machine learning techniques. The basic idea is to train a self learning system with manually annotated data from previous inspections so that the system learns to recognize underlying patterns of distress. Once the system is able to robustly identify intact infrastructure it can reduce the human amount of work by presenting only distress candidates to the operator. This helps to significantly speed up the inspection process and simultaneously reduces costs. Furthermore, inspection intervals can be reduced which helps to remedy deficiencies in time.

The system is applied to two types of infrastructure, namely water pipes and public roads, in order to show the versatility of the self learning system.