Integrated Digital Marine Image Analysis and Management - new solutions to handle large image collections in environmental monitoring and exploration

Steinbrink B, Schoening T, Brün D, Nattkemper TW. Integrated Digital Marine Image Analysis and Management - new solutions to handle large image collections in environmental monitoring and exploration. Presented at the GEOHAB, Salvador, Brazil

Rapid image processing and classification in underwater exploration using advanced high performance computing

Schoening T, Langenkämper D, Steinbrink B, Brün D, Nattkemper TW. Rapid image processing and classification in underwater exploration using advanced high performance computing. In: Proc. of the IEEE Oceans. Washington DC, USA: IEEE; In Press.Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0:2 sec per image (4224 2376 pixel) on average, which paves the way to real time online applications

Rapid image processing and classification in underwater exploration using advanced high performance computing

Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0:2 sec per image (4224 x 2376 pixel) on average, which paves the way to real time online applications

Image-based marine resource exploration and biodiversity assessment with MAMAS (Marine data Asset Management and Analysis System)

Schoening T, Brün D, Kuhn T, Nattkemper TW. Image-based marine resource exploration and biodiversity assessment with MAMAS (Marine data Asset Management and Analysis System). Presented at the UMI (Underwater Mining Institute), Lisbon, Portugal

Ultra-fast segmentation and quantification of poly-metallic nodule coverage in high-resolution digital images

Schoening T, Steinbrink B, Brün D, Kuhn T, Nattkemper TW. Ultra-fast segmentation and quantification of poly-metallic nodule coverage in high-resolution digital images. In: Proceedings of the UMI 2013. In Press.The exploration of the seafloor regarding the abundances of poly-metallic nodules has received growing attention recently. Image-based exploration using camera-equipped deep-sea observation systems has been introduced successfully to increase spatial resolution in the exploration process but the evaluation and interpretation of the huge amount of image data creates a serious bottleneck. In this paper we present a new software solution to the problem of automatic detection and segmentation of poly-metallic nodules in underwater images, which is not only accurate but so fast that real time image analysis is definitely in reach. Thus, an application on an AUV (Autonomous Underwater Vehicle) seems possible in the near future

A data-based model for condition monitoring and maintenance planning for protective coating systems for wind tower structures

Momber AW, Nattkemper TW, Langenkämper D, et al. A data-based model for condition monitoring and maintenance planning for protective coating systems for wind tower structures. Renewable Energy. 2022.The application of protective coating systems is the major measure against the corrosion of steel structures for onshore wind turbines. The organic coatings are, however, susceptible to atmospheric exposure and tend to deteriorate during the operation. At the same time, onshore turbines become more powerful and require taller and more resistant tower structures. The inspection and condition monitoring of protective coating systems on large onshore turbines (in excess of 120 m height) is a demanding and time-consuming procedure and requires high human effort. The rapid developments in digitization and data analysis offer opportunities to notably increase the efficiency of monitoring processes and to develop (semi-)automated standardized procedures. The paper describes a data-oriented approach to utilize digital data for the monitoring and maintenance planning of surface protection systems of large onshore wind turbines. The proposed approach includes the following steps: the segmentation of an existing wind power structure into a number of reference areas based on an In-situ Virtual Twin; the definition of a local deterioration degree for each individual reference area; the annotation of image data; the use of heterogenous multi-modal data (image data, geodetical data, meteorological data, profile scanning data) as the sources for condition assessment and monitoring. An example procedure is exercised for a tower structure of an onshore wind power turbine in order to illustrate the practical relevance of the approach