Journal of Marine Science and Engineering

Research

25 pages, 12158 KiB

Open AccessArticle

Fractal Dimension as an Effective Feature for Characterizing Hard Marine Growth Roughness from Underwater Image Processing in Controlled and Uncontrolled Image Environments

by Franck Schoefs, Michael O’Byrne, Vikram Pakrashi, Bidisha Ghosh, Mestapha Oumouni, Thomas Soulard and Marine Reynaud

J. Mar. Sci. Eng. 2021, 9(12), 1344; https://doi.org/10.3390/jmse9121344 - 29 Nov 2021

Cited by 12 | Viewed by 2643

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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18 pages, 1510 KiB

Open AccessArticle

An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

by Kai Hu, Yanwen Zhang, Chenghang Weng, Pengsheng Wang, Zhiliang Deng and Yunping Liu

J. Mar. Sci. Eng. 2021, 9(7), 691; https://doi.org/10.3390/jmse9070691 - 24 Jun 2021

Cited by 24 | Viewed by 8998

Abstract

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete [...] Read more.

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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14 pages, 12662 KiB

Open AccessArticle

Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

by Qingliang Jiao, Ming Liu, Pengyu Li, Liquan Dong, Mei Hui, Lingqin Kong and Yuejin Zhao

J. Mar. Sci. Eng. 2021, 9(6), 570; https://doi.org/10.3390/jmse9060570 - 25 May 2021

Cited by 11 | Viewed by 2736

Abstract

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange [...] Read more.

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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17 pages, 1953 KiB

Open AccessArticle

Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

by Yannik Steiniger, Dieter Kraus and Tobias Meisen

J. Mar. Sci. Eng. 2021, 9(3), 239; https://doi.org/10.3390/jmse9030239 - 24 Feb 2021

Cited by 15 | Viewed by 2934

Abstract

The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, [...] Read more.

The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN’s discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN’s ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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19 pages, 1372 KiB

Open AccessArticle

Coral Image Segmentation with Point-Supervision via Latent Dirichlet Allocation with Spatial Coherence

by Xi Yu, Bing Ouyang and Jose C. Principe

J. Mar. Sci. Eng. 2021, 9(2), 157; https://doi.org/10.3390/jmse9020157 - 5 Feb 2021

Cited by 3 | Viewed by 2366

Abstract

Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially for underwater images data sets such as corals and marine [...] Read more.

Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially for underwater images data sets such as corals and marine animals. Therefore, the overreliance on labels is one of the main obstacles for widespread applications of deep learning methods. In order to overcome this need for large annotated dataset, this paper proposes a label-efficient deep learning framework for image segmentation using only very sparse point-supervision. Our approach employs a latent Dirichlet allocation (LDA) with spatial coherence on feature space to iteratively generate pseudo labels. The method requires, as an initial condition, a Wide Residual Network (WRN) trained with sparse labels and mutual information constraints. The proposed method is evaluated on the sparsely labeled coral image data set collected from the Pulley Ridge region in the Gulf of Mexico. Experiments show that our method can improve image segmentation performance against sparsely labeled samples and achieves better results compared with other semi-supervised approaches. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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15 pages, 10254 KiB

Open AccessArticle

Fishing Net Health State Estimation Using Underwater Imaging

by Wenliang Qiu, Vikram Pakrashi and Bidisha Ghosh

J. Mar. Sci. Eng. 2020, 8(9), 707; https://doi.org/10.3390/jmse8090707 - 11 Sep 2020

Cited by 12 | Viewed by 3243

Abstract

Fishing net cleanliness plays a critical role for aquaculture industry as bio-fouled nets restrict the flow of water through the net leading to a build-up of toxins and reduced oxygen levels within the pen, thereby putting the fish under increased stress. In this [...] Read more.

Fishing net cleanliness plays a critical role for aquaculture industry as bio-fouled nets restrict the flow of water through the net leading to a build-up of toxins and reduced oxygen levels within the pen, thereby putting the fish under increased stress. In this paper, we proposed an underwater fishing Net Health State Estimation (NHSE) method, which can automatically analyze the degree of fouling on the net through underwater image analysis using remotely operated vehicles (ROV) images, and calculate a blocking percentage metric of each net opening. The level of fouling estimated through this method help the operators decide on the need of cleaning or maintenance schedule. There are mainly six modules in the proposed NHSE method, namely user interaction, distortion correction, underwater image dehazing, marine growth segmentation, net-opening structure analysis, and blocked percentage estimation. To evaluate the proposed NHSE method, we collected and labeled several underwater images in Mulroy Bay, Ireland with pixel-wise annotations. In order to verify the universality and robustness of the algorithm, we simulated and built a virtual fishing farm, and, on this basis, collected and labeled fishing net images under different environmental conditions. Seven evaluation metrics are introduced to demonstrate the effectiveness and advantages of the proposed method. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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13 pages, 1970 KiB

Open AccessArticle

A-Priori Calibration of a Structured Light Underwater 3D Sensor

by Christian Bräuer-Burchardt, Christoph Munkelt, Ingo Gebhart, Matthias Heinze, Stefan Heist, Peter Kühmstedt and Gunther Notni

J. Mar. Sci. Eng. 2020, 8(9), 635; https://doi.org/10.3390/jmse8090635 - 20 Aug 2020

Cited by 7 | Viewed by 2549

Abstract

In this study, we introduce a new calibration method for underwater optical stereo scanners. It uses air calibration, additional underwater parameters, and extended camera modeling. The new methodology can be applied to both passive photogrammetric and structured light three-dimensional (3D) scanning systems. The [...] Read more.

In this study, we introduce a new calibration method for underwater optical stereo scanners. It uses air calibration, additional underwater parameters, and extended camera modeling. The new methodology can be applied to both passive photogrammetric and structured light three-dimensional (3D) scanning systems. The novel camera model uses a variable principal distance depending on the radial distance to the principal point instead of two-dimensional distortion functions. This allows for an initial improvement of 3D reconstruction quality. In a second step, certain underwater-specific parameters—such as refraction indices, glass thickness, and view-port distances—are determined. Finally, a correction function for the entire measurement volume can be obtained from a few underwater measurements. Its application further improves the measurement accuracy. Measurement examples show the performance of the new calibration method in comparison to current underwater calibration strategies. A discussion of the possibilities and limits of the new calibration method and an outlook for future work complete this work. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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16 pages, 5807 KiB

Open AccessArticle

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

by Iman Abaspur Kazerouni, Gerard Dooly and Daniel Toal

J. Mar. Sci. Eng. 2020, 8(6), 449; https://doi.org/10.3390/jmse8060449 - 19 Jun 2020

Cited by 14 | Viewed by 3816

Abstract

Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)²PCA and A-KAZE [...] Read more.

Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)²PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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18 pages, 13278 KiB

Open AccessArticle

Applications of Virtual Data in Subsea Inspections

by Michael O’Byrne, Bidisha Ghosh, Franck Schoefs and Vikram Pakrashi

J. Mar. Sci. Eng. 2020, 8(5), 328; https://doi.org/10.3390/jmse8050328 - 7 May 2020

Cited by 17 | Viewed by 3335

Abstract

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented [...] Read more.

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented demonstrating how the inspection process can be enhanced through the use of virtual data. The first case study looks at how immersive virtual underwater scenes can be created to help divers and inspectors plan and implement real-world inspections. The second case study shows an example where deep learning-based computer vision methods are trained on datasets comprised of instances of virtual damage, specifically instances of barnacle fouling on the surface of a ship hull. The trained deep models are then applied to detect real-world instances of biofouling with promising results. The final case study shows how image-based damage detection methods can be calibrated using virtual images of damage captured under various simulated levels of underwater visibility. The work emphasizes the value of virtual data in creating a more efficient, safe and informed underwater inspection campaign for a wide range of built infrastructure, potentially leading to better monitoring, inspection and lifetime performance of such underwater structures. Full article

(This article belongs to the Special Issue Underwater Computer Vision and Image Processing)

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