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Advanced Sensor Applications in Marine Objects Recognition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 17293

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Prof. Dr. Bogusław Cyganek

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Faculty of Computer Science, Electronics and Telecommunication, Department of Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: computer vision; machine learning; artificial intelligence; software development in C++ and Python
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ocean, covering over 70% of our planet and reaching depths of nearly 11,000 meters, is the last largely unexplored environment on Earth. Since a vast majority of electromagnetic waves, including light, penetrate the ocean depths only to a very limited degree, sensing and exploring the immense ocean environment presents exceptional challenges. Despite great advances in sensing technologies, there are still serious challenges in their successful applications in various domains of marine recognition. Hence, development of novel methods, joining modern sensing techniques operating with diverse signal types, is of primary interest for the research and marine industry.

The purpose of this Special Issue is to provide a platform for information exchange and knowledge sharing, as well as to gather the latest research achievements, in the broad subject of sensor applications on marine recognition viewed from both the science and industry perspectives.

Original submissions from all areas related to sensor applications on marine recognition are welcome. Topics of interest include but are not limited to the following ones.

Prof. Dr. Bogusław Cyganek
Guest Editor

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Keywords

Marine sensors technologies
Marine recognition for biology
Underwater search and exploration
Sensing for autonomous underwater drones
Underwater drone navigation
Deep learning and AI methods in marine recognition
Deep-sea multispectral, hyperspectral and acoustic sensing
Computer vision and image processing methods for marine recognition
Automated detection, classification, and segmentation of marine objects
Underwater image and video restoration methods
Tensor based methods for multidimensional signal processing
Deep-sea optical imaging technologies
Acoustic imaging in marine recognition
Recognition from sidescan and multibeam sonars
Restoration and filtering of the sidescan and multibeam sonar signals
Seismic monitoring and imaging
Multichannel seismic technologies
Data management, annotations, access, visualization, and sharing

Published Papers (10 papers)

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Research

11 pages, 12148 KiB

Open AccessArticle

Effect of Light-Emitting Grid Panel on Indoor Aquaculture for Measuring Fish Growth

by Nguyen Ngoc Huynh, Myoungjae Jun and Hieyong Jeong

Sensors 2024, 24(3), 852; https://0-doi-org.brum.beds.ac.uk/10.3390/s24030852 - 28 Jan 2024

Viewed by 629

Abstract

This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in real time. Automatic fish size measurement technology is one of the essential elements for unmanned aquaculture. Under the condition of labor shortage, operators have much fatigue because they use a primitive method that samples the size and weight of fish just before fish shipment and measures them directly by humans. When this kind of process is automated, the operator’s fatigue can be significantly reduced. Above all, after measuring the fish growth, predicting the final fish shipment date is possible by estimating how much feed and time are required until the fish becomes the desired size. In this study, a video camera and a developed light-emitting grid panel were installed in indoor aquaculture to acquire images of fish, and the size measurement of a mock-up fish was implemented using the proposed method. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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24 pages, 20987 KiB

Open AccessArticle

On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

by Marcin Blachnik, Roman Przyłucki, Sławomir Golak, Piotr Ściegienka and Tadeusz Wieczorek

Sensors 2023, 23(15), 6806; https://0-doi-org.brum.beds.ac.uk/10.3390/s23156806 - 30 Jul 2023

Cited by 2 | Viewed by 945

Abstract

Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element method with the results of physical tests. Additionally, the paper discusses the methodology of simplifying the computational model, allowing for an almost three times reduction in the calculation time without affecting model quality. The article also presents and discusses the methodology for generating a dataset for the discrimination of UXO/non-UXO objects. According to that methodology, a dataset is generated and described in detail including assumptions on objects considered as UXO and nonUXO. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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15 pages, 15368 KiB

Open AccessArticle

Frequency-Domain Reverse-Time Migration with Analytic Green’s Function for the Seismic Imaging of Shallow Water Column Structures in the Arctic Ocean

by Seung-Goo Kang and U Geun Jang

Sensors 2023, 23(14), 6622; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146622 - 23 Jul 2023

Viewed by 754

Abstract

Seismic oceanography can provide a two- or three-dimensional view of the water column thermocline structure at a vertical and horizontal resolution from the multi-channel seismic dataset. Several seismic imaging methods and techniques for seismic oceanography have been presented in previous research. In this study, we suggest a new formulation of the frequency-domain reverse-time migration method for seismic oceanography based on the analytic Green’s function. For imaging thermocline structures in the water column from the seismic data, our proposed seismic reverse-time migration method uses the analytic Green’s function for numerically calculating the forward- and backward-modeled wavefield rather than the wave propagation modeling in the conventional algorithm. The frequency-domain reverse-time migration with analytic Green’s function does not require significant computational memory, resources, or a multifrontal direct solver to calculate the migration seismic images as like conventional reverse-time migration. The analytic Green’s function in our reverse-time method makes it possible to provide a high-resolution seismic water column image with a meter-scale grid size, consisting of full-band frequency components for a modest cost and in a low-memory environment for computation. Our method was applied to multi-channel seismic data acquired in the Arctic Ocean and successfully constructed water column seismic images containing the oceanographic reflections caused by thermocline structures of the water mass. From the numerical test, we note that the oceanographic reflections of the migrated seismic images reflected the distribution of Arctic waters in a shallow depth and showed good correspondence with the anomalies of measured temperatures and calculated reflection coefficients from each XCDT profile. Our proposed method has been verified for field data application and accuracy of imaging performance. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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27 pages, 5188 KiB

Open AccessArticle

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

by Alberto Monterroso Muñoz, Maria-Jose Moron-Fernández, Daniel Cascado-Caballero, Fernando Diaz-del-Rio and Pedro Real

Sensors 2023, 23(10), 4986; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104986 - 22 May 2023

Cited by 5 | Viewed by 2660

Abstract

Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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28 pages, 7923 KiB

Open AccessArticle

Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling

by Patryk Biernacik, Witold Kazimierski and Marta Włodarczyk-Sielicka

Sensors 2023, 23(8), 3941; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083941 - 13 Apr 2023

Cited by 4 | Viewed by 1620

Abstract

Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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22 pages, 7730 KiB

Open AccessArticle

A Network Model for Detecting Marine Floating Weak Targets Based on Multimodal Data Fusion of Radar Echoes

by Guoxing Duan, Yunhua Wang, Yanmin Zhang, Shuya Wu and Letian Lv

Sensors 2022, 22(23), 9163; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239163 - 25 Nov 2022

Cited by 1 | Viewed by 1467

Abstract

Due to the interaction between floating weak targets and sea clutter in complex marine environments, it is necessary to distinguish targets and sea clutter from different dimensions by designing universal deep learning models. Therefore, in this paper, we introduce the concept of multimodal data fusion from the field of artificial intelligence (AI) to the marine target detection task. Using deep learning methods, a target detection network model based on the multimodal data fusion of radar echoes is proposed. In the paper, according to the characteristics of different modalities data, the temporal LeNet (T-LeNet) network module and time-frequency feature extraction network module are constructed to extract the time domain features, frequency domain features, and time-frequency features from radar sea surface echo signals. To avoid the impact of redundant features between different modalities data on detection performance, a Self-Attention mechanism is introduced to fuse and optimize the features of different dimensions. The experimental results based on the publicly available IPIX radar and CSIR datasets show that the multimodal data fusion of radar echoes can effectively improve the detection performance of marine floating weak targets. The proposed model has a target detection probability of 0.97 when the false alarm probability is

10^{- 3}

under the lower signal-to-clutter ratio (SCR) sea state. Compared with the feature-based detector and the detection model based on single-modality data, the new model proposed by us has stronger detection performance and universality under various marine detection environments. Moreover, the transfer learning method is used to train the new model in this paper, which effectively reduces the model training time. This provides the possibility of applying deep learning methods to real-time target detection at sea. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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Graphical abstract

14 pages, 1038 KiB

Open AccessArticle

Image Semantic Segmentation of Underwater Garbage with Modified U-Net Architecture Model

by Lifu Wei, Shihan Kong, Yuquan Wu and Junzhi Yu

Sensors 2022, 22(17), 6546; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176546 - 30 Aug 2022

Cited by 2 | Viewed by 2086

Abstract

Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. To assist underwater robots in locating and recognizing underwater garbage objects efficiently, a modified U-Net-based architecture consisting of a deeper contracting path and an expansive path is proposed to accomplish end-to-end image semantic segmentation. In addition, a dataset for underwater garbage semantic segmentation is established. The proposed architecture is further verified in the underwater garbage dataset and the effects of different hyperparameters, loss functions, and optimizers on the performance of refining the predicted segmented mask are examined. It is confirmed that the focal loss function will lead to a boost in solving the target–background unbalance problem. Eventually, the obtained results offer a solid foundation for fast and precise underwater target recognition and operations. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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23 pages, 2834 KiB

Open AccessArticle

The Impact of Data Augmentations on Deep Learning-Based Marine Object Classification in Benthic Image Transects

by Mingkun Tan, Daniel Langenkämper and Tim W. Nattkemper

Sensors 2022, 22(14), 5383; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145383 - 19 Jul 2022

Cited by 1 | Viewed by 1547

Abstract

Data augmentation is an established technique in computer vision to foster the generalization of training and to deal with low data volume. Most data augmentation and computer vision research are focused on everyday images such as traffic data. The application of computer vision techniques in domains like marine sciences has shown to be not that straightforward in the past due to special characteristics, such as very low data volume and class imbalance, because of costly manual annotation by human domain experts, and general low species abundances. However, the data volume acquired today with moving platforms to collect large image collections from remote marine habitats, like the deep benthos, for marine biodiversity assessment and monitoring makes the use of computer vision automatic detection and classification inevitable. In this work, we investigate the effect of data augmentation in the context of taxonomic classification in underwater, i.e., benthic images. First, we show that established data augmentation methods (i.e., geometric and photometric transformations) perform differently in marine image collections compared to established image collections like the Cityscapes dataset, showing everyday traffic images. Some of the methods even decrease the learning performance when applied to marine image collections. Second, we propose new data augmentation combination policies motivated by our observations and compare their effect to those proposed by the AutoAugment algorithm and can show that the proposed augmentation policy outperforms the AutoAugment results for marine image collections. We conclude that in the case of small marine image datasets, background knowledge, and heuristics should sometimes be applied to design an effective data augmentation method. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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22 pages, 4504 KiB

Open AccessArticle

A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

by Atif Naseer, Enrique Nava Baro, Sultan Daud Khan and Yolanda Vila

Sensors 2022, 22(12), 4441; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124441 - 12 Jun 2022

Cited by 7 | Viewed by 1694

Abstract

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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13 pages, 3485 KiB

Open AccessArticle

Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding

by Taku Yutani, Oak Yono, Tatsu Kuwatani, Daisuke Matsuoka, Junji Kaneko, Mitsuko Hidaka, Takafumi Kasaya, Yukari Kido, Yoichi Ishikawa, Toshiaki Ueki and Eiichi Kikawa

Sensors 2022, 22(9), 3198; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093198 - 21 Apr 2022

Cited by 5 | Viewed by 2437

Abstract

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability. Full article

(This article belongs to the Special Issue Advanced Sensor Applications in Marine Objects Recognition)

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