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Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to “traditional” annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections. [ABSTRACT FROM AUTHOR]

The application of protective coating systems is the major measure against the corrosion of stationary marine steel structures. During their service, protective coatings are exposed to various loads, and their protectiveness deteriorates. Digital visual inspection tools are becoming a standard for the condition monitoring of coatings for large and complex structures. One of the challenges with the use of these tools is the accumulation of large amounts of visual data to be evaluated and assessed in short time intervals. A comprehensive interpretation of such image collections requires detection, classification, and quantification processes, which are usually performed by domain experts (coating inspectors). However, the data volume and the rich content of the images make the support by software tools inevitable. This contribution defines requirements for image data annotation, and it presents for the first time the application of an online exploration and annotation tool to coating condition monitoring data and its introduction into a prescriptive coating maintenance model. The software architecture of the system is described, special features are illustrated with different use-cases, and future developments are discussed. The utilization of machine learning-based assessment tools is presented. • First integrative data-driven approach for online analysis of large volumes of visual monitoring data. • Integration of visual data into a prescriptive maintenance model. • Coating deterioration modeling based on pixel footprints of images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]