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In the project SECMAN " SECurity MANual " a simple four-step procedure for the identification of critical road infrastructures, assessment of these infrastructures regarding various man-made threats and the determination of effective protection measures was developed. These methodologies are summarized and combined into a comprehensive best-practice manual which allows for a trans-national structured and holistic security-risk-management approach for owners and operators of road infrastructures in Europe. This paper presents the developed methodology starting from the assessment procedures of a network's criticality over an object's attractiveness and vulnerability to the selection process of appropriate protection measures.
Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.