Image-based Damage Assessment for Underwater Inspections - From theory to implementation



Inspection is crucial to the management of ageing infrastructure. Visual information on structures is regularly collected but very little work exists on its organised and quantitative analysis, even though image processing can significantly enhance these inspection processes and transfer real financial and safety benefits to the managers, owners and users. Additionally, new opportunities exist in the fast evolving sectors of wind and wave energy to add value to image-based inspection techniques. This book is a first for structural engineers and inspectors who wish to harness the full potential of cameras as an inspection tool. It is particularly directed to the inspection of offshore and marine structures and the application of image-based methods in underwater inspections. It outlines a set of best practice guidelines for obtaining imagery, then the fundamentals of image processing are covered along with several image processing techniques which can be used to assess multiple damage forms: crack detection, corrosion detection, and depth analysis of marine growth on offshore structures. The book provides benchmark performance measures for these techniques under various visibility conditions using an image repository which will help inspectors to envisage the effectiveness of the techniques when applied. MATLAB® scripts and access to the underwater image repository are included so readers can run these techniques themselves. Practising engineers and managers of infrastructure assets are guided in image processing based inspection. Researchers can use this book as a primer, and it also suits advanced graduate courses in infrastructure management or on applied image processing.
... It is difficult to compare the quantitative results presented in this paper with other studies, as the proposed methodology and the detection method is new. However, relevant works on varied, controlled lighting conditions, created in the laboratory [15] or synthetically [45], indicate that the ROC space, region of operation, and best performance points established from image processing [17,[56][57][58] are similar to what has been obtained in this paper. Fractal estimates on corrosion images [59] indicate how this feature can be an indicator of gradual degradation. ...
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Hard marine growth is an important process that affects the design and maintenance of floating offshore wind turbines. A key parameter of hard biofouling is roughness since it considerably changes the level of drag forces. Assessment of roughness from on-site inspection is required to improve updating of hydrodynamic forces. Image processing is rapidly developing as a cost effective and easy to implement tool for observing the evolution of biofouling and related hydrodynamic effects over time. Despite such popularity; there is a paucity in literature to address robust features and methods of image processing. There also remains a significant difference between synthetic images of hard biofouling and their idealized laboratory approximations in scaled wave basin testing against those observed in real sites. Consequently; there is a need for such a feature and imaging protocol to be linked to both applications to cater to the lifetime demands of performance of these structures against the hydrodynamic effects of marine growth. This paper proposes the fractal dimension as a robust feature and demonstrates it in the context of a stereoscopic imaging protocol; in terms of lighting and distance to the subject. This is tested for synthetic images; laboratory tests; and real site conditions. Performance robustness is characterized through receiver operating characteristics; while the comparison provides a basis with which a common measure and protocol can be used consistently for a wide range of conditions. The work can be used for design stage as well as for lifetime monitoring and decisions for marine structures, especially in the context of offshore wind turbines.
... Currently, underwater imaging technology mainly includes optical imaging and sonar imaging [1]. Optical imaging has a better resolution, but its resolution poor under the drowning environment and the imaging distance is relatively close [2]. Sonar imaging has the advantage of long operating distance and strong penetration ability, which is especially suitable for underwater environment. ...
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Automatic detection of underwater objects by sonar images is an important and challenging topic in applications of Autonomous Underwater Vehicle (AUV) under the complex marine environment. A detection method is proposed based on Multi-Scale Multi-Column Convolution Neural Networks (MSMC-CNNs). Firstly, the Multi-Scale Multi-Column CNNs is used to form an encoder network for extracting multi-scale features of the sonar image. Secondly, the bicubic linear interpolation algorithm is used as the deconvolution process of the decoder networks to restore the sonar image size and resolution. Moreover, a novel transfer learning manner based on progressive fine-tuning to accelerate the model training. Finally, the proposed method is validated on the sonar image dataset and is compared with other existing detection methods. The pixel accuracy (PA) of MSMC-CNNs for different categories sonar image is over 95%. The experiment results show that the MSMC-CNNs model has better detection effect and more robustness to noise.
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