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Journal articles on the topic 'Side-scan sonar'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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1

Maccabee, Bruce S., and Charles E. Bell. "Bistatic side scan sonar." Journal of the Acoustical Society of America 92, no. 1 (1992): 626. http://dx.doi.org/10.1121/1.404084.

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2

Ye, Xiufen, Haibo Yang, Chuanlong Li, Yunpeng Jia, and Peng Li. "A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex." Remote Sensing 11, no. 11 (2019): 1281. http://dx.doi.org/10.3390/rs11111281.

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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.
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3

Daniel, S., F. Le Leannec, C. Roux, B. Soliman, and E. P. Maillard. "Side-scan sonar image matching." IEEE Journal of Oceanic Engineering 23, no. 3 (1998): 245–59. http://dx.doi.org/10.1109/48.701197.

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4

Anderson, Aubrey L., and Shufa Dwan. "Simulation of side‐scan sonar." Journal of the Acoustical Society of America 80, S1 (1986): S112. http://dx.doi.org/10.1121/1.2023566.

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5

Thorpe, S. A., and A. J. Hall. "Nearshore Side-Scan Sonar Studies." Journal of Atmospheric and Oceanic Technology 10, no. 5 (1993): 778–83. http://dx.doi.org/10.1175/1520-0426(1993)010<0778:nssss>2.0.co;2.

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6

Maccabee, Bruce, and Charles E. Bell. "4975887 Bistatic side scan sonar." Deep Sea Research Part B. Oceanographic Literature Review 38, no. 8 (1991): 701–2. http://dx.doi.org/10.1016/s0198-0254(06)80684-9.

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7

McLarty, Mindy J., Daniel Gonzalez-Socoloske, Anmari Alvarez-Alemán, and Jorge Angulo-Valdés. "Manatee habitat characterization using side-scan sonar." Journal of the Marine Biological Association of the United Kingdom 100, no. 1 (2019): 173–79. http://dx.doi.org/10.1017/s0025315419000973.

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AbstractIdentifying benthic substrates is important to researchers studying aquatic organisms in fresh and salt water systems. Benthic substrates are often not visible from the surface making it necessary to find another method to gather these data. Previous research has demonstrated that low cost side-scan sonar is a reliable way to identify hard substrates, such as rock and gravel, in a small, freshwater stream. In this study, the reliability of the side-scan sonar to accurately identify softer substrates such as grass and mud was tested in a large, brackish lagoon system. A total area of 11.55 km2 was surveyed with the sonar. Videos and pictures were taken at various points to groundtruth the sonar images and provide a measure of accuracy. Five substrate types were identified: dense seagrass, sparse seagrass, mangrove soil, mangrove soil with rock, and silt. Unidentifiable substrates were classified as unknown. A manually zoned benthic substrate map was created from the sonar recordings. Dense seagrass was most accurately identified. Sparse seagrass was the least accurately identified. A bathymetric map was also created from the sonar recordings.
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8

Ge, Qiang, Fengxue Ruan, Baojun Qiao, Qian Zhang, Xianyu Zuo, and Lanxue Dang. "Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks." Electronics 10, no. 15 (2021): 1823. http://dx.doi.org/10.3390/electronics10151823.

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Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”.
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9

Sternlicht, Daniel D. "Historical development of side scan sonar." Journal of the Acoustical Society of America 141, no. 5 (2017): 4041. http://dx.doi.org/10.1121/1.4989335.

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10

Tan, Kuan Meng, Tien Fu Lu, and Amir Anvar. "Side Scan Sonar Modeling for Maritime Robotic Operations." Applied Mechanics and Materials 152-154 (January 2012): 1195–201. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1195.

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One of the key aspects in designing an Autonomous Underwater Vehicle (AUV) simulation framework is sensor modeling. This paper presents specifically the underwater sonar sensor modeling structure used in the proposed AUV simulation framework. This sensor model covers the mathematical aspects from the field of acoustics which mimics real world sensors. Simplified sonar signal models are widely used however rarely discussed in the literature. Based on this designed simulation framework, simple scenario using different sonar configuration is shown and discussed. This paper shows the formulation of a typical side-scan sonar with emphasis on the assumptions which leads to the simplification of the sonar model. The sonar sensor model is built based on a developed AUV test-bed which was done previously in the University of Adelaide.
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11

Mochtar, Agni, Firman Setiawan, and Shinatria Adhityatama. "SURVEI SIDE SCAN SONAR DALAM PENELITIAN ARKEOLOGI BAWAH AIR DI PERAIRAN SUNGAI: STUDI KASUS PADA DAERAH ALIRAN SUNGAI BRANTAS [SIDE SCAN SONAR SURVEY IN RIVERINE UNDERWATER ARCHAEOLOGICAL RESEARCH: CASE STUDY IN THE BRANTAS BASIN]." Naditira Widya 15, no. 2 (2021): 99–112. http://dx.doi.org/10.24832/nw.v15i2.459.

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Aplikasi metode geofisika menggunakan side scan sonar dalam penelitian arkeologi bawah air belum banyak dilakukan di Indonesia. Tulisan ini memaparkan penggunaan side scan sonar untuk pemetaan dasar sungai dan identifikasi tinggalan arkeologi di dasar sungai dalam penelitian “Sungai Brantas dalam Perspektif Lanskap Kultur Maritim”, serta interpretasi hasil survei side scan sonar tersebut dalam konteks kesejarahan. Selain itu, dalam tulisan ini akan dibahas potensi pengembangan penggunaan side scan sonar dalam penelitian arkeologi bawah air di Indonesia, terutama di perairan sungai. Akuisisi data dilakukan dengan menggunakan side scan sonar Starfish 450H dengan sistem posisi GNSS Trimble R8s. Sementara itu, interpretasi diperoleh dengan melakukan analisis terhadap data peta dan arsip Belanda untuk memahami konteks temporal dari objek yang dideteksi oleh alat side scan sonar. Survei berhasil menunjukkan sedimen di dasar sungai berupa lempung dan lanau, serta beberapa objek yang diduga sebagai bangkai kapal, yang diperkirakan berasal dari pasca abad ke-19 Masehi. Hasil survei side scan sonar menunjukkan tingkat akurasi cukup hingga tinggi dan dapat menjadi pendukung penelitian arkeologi bawah air yang efisien, terutama di perairan yang keruh.&#x0D; Side scan sonar survey as one of the geophysics methods is still scarcely applied in underwater archaeological research in Indonesia. This paper describes the application of side scan sonar survey in mapping riverbed and identifying underwater archaeological remains in the “Sungai Brantas in the Perspective of Maritime Cultural Landscape” project, as well as interpreting its historical context based on survey results. This paper also explores the development of utilizing side scan sonar in underwater archaeological research in Indonesia, particularly in rivers. Data was acquisitioned by using the side scan sonar Starfish 450H and GNSS Trimble R8s positioning system. The interpretation was drawn by analysing related Dutch old maps and archives to understand the historical context of the survey findings. The result shows clay and silt sediment covering most of the riverbed and a number of objects, possibly shipwrecks, estimated as from the nineteenth century. The survey result has a medium to high accuracy. Thus, this method is able to serve as an efficient instrument for underwater archaeological research, especially in the low-visibility waters.
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12

Efendi, Tria, Dikdik S. Mulyadi, Agung Prasetyo, Amril Amril, Adhi Kusuma, and Agus Iwan Santoso. "Komparasi Pengolahan Data Side Scan Sonar Menggunakan 2 (dua) Perangkat Lunak Triton Imaging Isis dan Sonarwiz (Studi Kasus Perairan Batam Kepulauan Riau)." Jurnal HIDROPILAR 5, no. 1 (2020): 29–34. http://dx.doi.org/10.37875/hidropilar.v5i1.157.

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Survei dan pemetaan laut yang sangat diperlukan dalam berbagai aplikasi kelautan dapat dilakukan dengan berbagai cara, salah satunya dengan proses pencitraan dasar laut. Proses pencitraan dasar laut dapat dilakukan dengan berbagai cara pula, diantaranya pencitraan dengan menggunakan instrumen Side Scan Sonar. Perangkat lunak Triton imaging Isis dan Sonarwiz sebagai perangkat pengolahan data Side Scan Sonar diharapkan dapat memberikan gambaran nyata citra dasar laut sehingga dapat meningkatkan kinerja dari pelaksanaan survei Side Scan Sonar. Tugas Akhir ini akan memberikan penjelasan tentang perbandingan pengolahan data Side Scan Sonar dengan menggunakan perangkat lunak Triton Imaging Isis dan Sonarwiz, sehingga menghasilkan data gambaran dasar laut. Hasil pengolahan data citra Side Scan Sonar menggunakan Triton Imaging Isis dan Sonarwiz dapat dijadikan informasi posisi pipa gas dasar laut dan diplot untuk dijadikan sebuah peta dalam bentuk lembar lukis teliti.
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13

Bikonis, Krzysztof, Marek Moszynski, and Zbigniew Lubniewski. "Application of Shape From Shading Technique for Side Scan Sonar Images." Polish Maritime Research 20, no. 3 (2013): 39–44. http://dx.doi.org/10.2478/pomr-2013-0033.

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Abstract Digital signal processing technology has revolutionized a way of processing, visualisation and interpretation of data acquired by underwater systems. Through many years side scan sonars were one of the most widely used imaging systems in the underwater environment. Although they are relatively cheap and easy to deploy, more powerful sensors like multibeam echo sounders and sonars are widely used today and deliver 3D bathymetry of sea bottom terrain. Side scan sonar outputs data usually in a form of grey level 2D acoustic images but the analysis of such pictures performed by human eye allows creating semi-spatial impressions of seafloor relief and morphology. Hence the idea of post-processing the side scan sonar data in a manner similar to human eye to obtain 3D visualisation. In recently developing computer vision systems the shape from shading approach is well recognized technique. Applying it to side scan sonar data is challenging idea used by several authors. In the paper, some further extensions are presented. They rely on processing the backscattering information of each footprint (pixel in sonar image) along with its surroundings. Additionally, a current altitude is estimated from the size of shadow areas. Both techniques allow constructing 3D representation of sea bottom relief or other investigated underwater objects.
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14

Lucchetti, Alessandro, Emilio Notti, Antonello Sala, and Massimo Virgili. "Multipurpose use of side-scan sonar technology for fisheries science." Canadian Journal of Fisheries and Aquatic Sciences 75, no. 10 (2018): 1652–62. http://dx.doi.org/10.1139/cjfas-2017-0359.

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This study illustrates some potential applications of side-scan sonar to explain issues related to fisheries science. Side-scan sonar enabled identification of the footprint of different trawl types. It showed that a twin trawl sweeps a 30% greater area than a traditional trawl and that a semipelagic trawl door has more limited impact than a traditional bottom otter trawl. The side-scan sonar enabled detection and characterization of the interaction of trawl gear with the seafloor. It demonstrated the cod end is floating above the sea bottom during the tow, while the doors and clumps, sweeps and bridles have the most damaging effect on the seafloor. Side-scan sonar was used to assess the interaction between active and passive gear and between trawls and pipelines. It has been able to detect illegal fishing activity in marine protected areas and has been a valuable tool to resolve disputes between different sectors. Side-scan sonar was finally tested as a suitable tool for fish school detection and counting. Side-scan sonar emerged as a flexible tool to tackle rapidly a number of issues related to fishing impact, technology, and biology.
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15

Peng, Lei. "Adaptive De-Noising Approach for Underwater Side Scan Sonar Image." Applied Mechanics and Materials 373-375 (August 2013): 509–12. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.509.

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It is difficult to detect the edges of objects in side scan sonar images due to the complex background, bad contrast and deteriorate edges. Therefore, it is important to remove noise from side scan sonar images. The traditional de-noising methods for optical images may not work well on the sonar image. In this paper, an adaptive de-noising approach is used. The side scan sonar image is first filtered using mean filter to remove the rough noise, then a weighted function is generated using spatial distance filter and intensity distance filter. The parameters are adaptive according to the sonar image. The experimental results indicate that it is an effective de-noising method for underwater sonar image.
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16

Yang, Dianyu, Can Wang, Chensheng Cheng, Guang Pan, and Feihu Zhang. "Semantic Segmentation of Side-Scan Sonar Images with Few Samples." Electronics 11, no. 19 (2022): 3002. http://dx.doi.org/10.3390/electronics11193002.

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Underwater sensing and detection still rely heavily on acoustic equipment, known as sonar. As an imaging sonar, side-scan sonar can present a specific underwater situation in images, so the application scenario is comprehensive. However, the definition of side scan sonar is low; many objects are in the picture, and the scale is enormous. Therefore, the traditional image segmentation method is not practical. In addition, data acquisition is challenging, and the sample size is insufficient. To solve these problems, we design a semantic segmentation model of side-scan sonar images based on a convolutional neural network, which is used to realize the semantic segmentation of side-scan sonar images with few training samples. The model uses a large convolution kernel to extract large-scale features, adds a parallel channel using a small convolution kernel to obtain multi-scale features, and uses SE-block to focus on the weight of different channels. Finally, we verify the effect of the model on the self-collected side-scan sonar dataset. Experimental results show that, compared with the traditional lightweight semantic segmentation network, the model’s performance is improved, and the number of parameters is relatively small, which is easy to transplant to AUV.
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17

Joko Rachimzah, Dwi, Eka Djunarsjah, Agung Prasetyo, and Dikdik S Mulyadi. "Pengolahan Data Side Scan Sonar Edgetech 4200 Menggunakan Perangkat Lunak Triton Imaging Studi Kasus Perairan Pulau Kangean Laut Bali." Jurnal HIDROPILAR 2, no. 2 (2016): 111–17. http://dx.doi.org/10.37875/hidropilar.v2i2.47.

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Abstrak - Survei dan pemetaan laut yang sangat diperlukan dalam berbagai aplikasi kelautan dapat dilakukan dengan berbagai cara, salah satunya dengan proses pencitraan dasar laut. Proses pencitraan dasar laut dapat dilakukan dengan berbagai cara pula, diantaranya pencitraan dengan menggunakan instrumen side scan sonar. Adanya perangkat lunak Triton Imaging Isis diharapkan dapat meningkatkan kinerja dari pelaksanaan survei side scan sonar.&#x0D; Tugas Akhir ini akan memberikan penjelasan tentang bagaimanakah pengolahan data Side Scan Sonar dengan menggunakan perangkat lunak Triton Imaging Isis, sehingga menghasilkan data gambaran topografi dasar laut.&#x0D; Hasil pengolahan data citra Side Scan Sonar menggunakan Triton Imaging Isis dapat dijadikan informasi posisi pipa gas dasar laut dan di plot untuk dijadikan sebuah peta dalam bentuk lembar lukis teliti.
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18

Service, M., and B. H. Magorrian. "The Extent and Temporal Variation of Disturbance to Epibenthic Communities in Strangford Lough, Northern Ireland." Journal of the Marine Biological Association of the United Kingdom 77, no. 4 (1997): 1151–64. http://dx.doi.org/10.1017/s0025315400038686.

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Side-scan sonar and underwater video were used to determine the impact of a trawl fishery on an epibenthic community associated with the horse mussel, Modiolus modiolus in a Northern Ireland sea lough. The presence of marks caused by trawl otter-boards on the sediments could be clearly seen using side-scan sonar and changes to the epibenthos are described from the video survey. It is apparent from the side-scan sonar survey that changes have occurred in the structure of the superficial sediments on heavily trawled areas. However, there was no clear indication of temporal changes. The utility of side-scan sonar coupled with GIS techniques to detect temporal and spatial effects is discussed.
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19

Prokhorov, I. V., and A. A. Sushchenko. "Imaging Based on Signal from Side-Scan Sonar." Applied Mechanics and Materials 756 (April 2015): 678–82. http://dx.doi.org/10.4028/www.scientific.net/amm.756.678.

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In this paper authors researched problems about construction of sonar images which based on signal from side-scan sonar. Authors use explicit formula for signal processing. It includes influence of volume scattering in the medium. Moreover, authors use an additional filter for signal. It is logarithm-filtration.
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20

Gwon, Dae-Hyeon, Joowan Kim, Moon Hwan Kim, Ho Gyu Park, Tae Yeong Kim, and Ayoung Kim. "Side Scan Sonar based Pose-graph SLAM." Journal of Korea Robotics Society 12, no. 4 (2017): 385–94. http://dx.doi.org/10.7746/jkros.2017.12.4.385.

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21

Johnson, H. Paul, and Maryann Helferty. "The geological interpretation of side-scan sonar." Reviews of Geophysics 28, no. 4 (1990): 357. http://dx.doi.org/10.1029/rg028i004p00357.

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22

Mason, D. C., T. P. LeBas, I. Sewell, and C. Angelikaki. "Deblurring of GLORIA side-scan sonar images." Marine Geophysical Researches 14, no. 2 (1992): 125–36. http://dx.doi.org/10.1007/bf01204283.

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23

Blake, Vanessa S. "The simulation of side-scan sonar images." Archaeological Prospection 2, no. 1 (1995): 29–56. http://dx.doi.org/10.1002/1099-0763(199503)2:1<29::aid-arp6140020105>3.0.co;2-p.

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24

Lubis, Muhammad Zainuddin, Husnul Kausarian, and Wenang Anurogo. "Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 3 (2017): 230. http://dx.doi.org/10.24273/jgeet.2017.2.3.560.

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The importance of knowing the method for seabed detection using side-scan sonar images with sonar instrument is a much-needed requirement right now. This kind of threat also requires frequent sonar surveys in such areas. These survey operations need specific procedures and special equipment to ensure survey correctness. In this paper describes the method of observation and retrieval of marine imagery data using an acoustic signal method, to determine a target based on the sea. Side scan sonar is an instrument consisting of single beam transducer on both sides. Side scan sonar (SSS) is a sonar development that is able to show in two-dimensional images of the seabed surface with seawater conditions and target targets simultaneously. The side scan sonar data processing is performed through geometric correction to establish the actual position of the image pixel, which consists of bottom tracking, slant-range correction, layback correction and radiometric correction performed for the backscatter intensity of the digital number assigned to each pixel including the Beam Angle Correction (BAC), Automatic Gain Control (AGC), Time Varied Gain (TVG), and Empirical Gain Normalization (EGN).
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Yan, Jun, Junxia Meng, and Jianhu Zhao. "Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet." Remote Sensing 13, no. 5 (2021): 1024. http://dx.doi.org/10.3390/rs13051024.

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As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.
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Peng, Lei. "Efficient High-Precision Feature Extraction Method in Side Scan Sonar Image." Applied Mechanics and Materials 678 (October 2014): 197–200. http://dx.doi.org/10.4028/www.scientific.net/amm.678.197.

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Side scan sonar image has disadvantages such as low-resolution, poor image quality, random factors’? disturbance, these disadvantages cause the sonar image less visual perception, poor readability and many other shortcomings. In this paper, geometric correction method and image processing technology were studied, and also a data processing software was developed to improve the side scan sonar data processing from two aspects of data production accuracy and processing efficiency.
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27

Christensen, Jesper H., Lars V. Mogensen, and Ole Ravn. "Side-Scan Sonar Imaging: Real-Time Acoustic Streaming." IFAC-PapersOnLine 54, no. 16 (2021): 458–63. http://dx.doi.org/10.1016/j.ifacol.2021.10.131.

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28

STRIDE, A. H. "The first geological use of side-scan sonar." Geology Today 8, no. 4 (1992): 146–50. http://dx.doi.org/10.1111/j.1365-2451.1992.tb00389.x.

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29

Burguera, Antoni, and Gabriel Oliver. "High-Resolution Underwater Mapping Using Side-Scan Sonar." PLOS ONE 11, no. 1 (2016): e0146396. http://dx.doi.org/10.1371/journal.pone.0146396.

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30

Cuschieri, J. M., and M. Hebert. "Three-Dimensional Map Generation From Side-Scan Sonar Images." Journal of Energy Resources Technology 112, no. 2 (1990): 96–102. http://dx.doi.org/10.1115/1.2905729.

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The generation of three-dimensional (3-D) images and map building are essential components in the development of an autonomous underwater system. Although the direct generation of 3-D images is more efficient than the recovery of 3-D data from 2-D information, at present for underwater applications where sonar is the main form of remote sensing, the generation of 3-D images can only be achieved by either complex sonar systems or with systems which have a rather low resolution. In this paper an overview is presented on the type of sonar systems that are available for underwater remote sensing, and then a technique is presented which demonstrates how through simple geometric reasoning procedures, 3-D information can be recovered from side scan-type (2-D) data. Also presented is the procedure to perform map building on the estimated 3-D data.
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Nguyen, Van Duc, Ngoc Minh Luu, Quoc Khuong Nguyen, and Tien-Dung Nguyen. "Estimation of the Acoustic Transducer Beam Aperture by Using the Geometric Backscattering Model for Side-Scan Sonar Systems." Sensors 23, no. 4 (2023): 2190. http://dx.doi.org/10.3390/s23042190.

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In this paper, we propose an algorithm for estimating the beam aperture of the acoustic transducers by using the geometric backscattering model for side-scan sonar systems. The geometric backscattering model is developed to describe the propagation paths of the signal transmitted from the transducers towards the seabed and backscatters to the hydrophones. To evaluate our proposed algorithm, we have developed a side-scan sonar system. The side-scan sonar system uses two transducers, operating on two different frequencies and focusing on two different wave beams, to scan the images of the seabed. The proposed algorithm provides the estimated beam apertures of each transducer. Our obtained results agree quite well with the parameters provided by the manufacturers. Moreover, these results are used to calibrate the scanned images. We provide the scanned sonar 3D images of the Dong Do lakebed, Vietnam, to justify our proposal.
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Kumudham, R., and V. Rajendran. "Side scan sonar image data mapping with geographic reference system." International Journal of Engineering & Technology 7, no. 2.21 (2018): 410. http://dx.doi.org/10.14419/ijet.v7i2.21.12454.

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Many researchers have been done in classifying the surface of the sea floor. Only few concentrated in classifying the sediment layers of the sea floor and the target objects buried. Side Scan Sonar is one such tool, which is used in collecting the images of the seafloor. Sonar equipment transmits a low frequency signal, towards the surface of the seabed for target recognition. It is necessary to locate the area and positioning where the target is located whether it is a ship wreckage or plane crash, mine recognition etc. This paper is proposed to determine the location and position in user friendly matlab software environment, where the sonar image data is collected and is mapped with Global Reference System.
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33

Lubis, Muhammad Zainuddin, Wenang Anurogo, Hanah Khoirunnisa, Sudra Irawan, Oktavianto Gustin, and Arif Roziqin. "Using Side-Scan Sonar instrument to Characterize and map of seabed identification target in punggur sea of the Riau Islands, Indonesia." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 1 (2017): 1. http://dx.doi.org/10.24273/jgeet.2017.2.1.11.

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Punggur sea has many habitats, object, and structured of seabed with hight tide and wave. Side scan sonar is an underwater acoustic instrument for identification of seabed. This research aims to classify types of seabed and measure seabed identification into the sea water with grain size (dB), location, altitude (m) and target using side scan sonar instrument. This research also uses one types of side scan sonar in one places with 3 line of collecting data to get more variant seabed. Side scan sonar data of 20 km of side-scan sonar profiling (CM2, C-MAX Ltd, UK) with altitude max 20 m and a working acoustic frequency of 325 kHz with the zone is taken in the punggur sea (104°08.7102 E, 1°03.2448 N until 1°03.3977N 104°08.8133 E). The data side scan sonar processed using max view software to display the image of the seabed. Results of seabed imagery in the punggur sea on track 1 have Objects found on the ship coordinates 03.3101N 1 ° and 104 ° 08.7362 E with the highest gain value is 6 dB, altitude 18 m on ping 75. Linear regression has y = 0.7016x+12.952 with R2 = 0.4125 (41%). Track 2 has target 1 is the sunken object on the seabed, while objects in the form of sand can be seen clearly. Objects found on the sunken object coordinates 1°02.8143 N ° and 104°08.5228 E with highest gain value is 9 dB with altitude 17.7 m and data ping 69. Linear regression has y = 0.2093+12.577 with R2 = 0.2093 (20%). Track 3 has Target 1 is the ship object on the seabed, while objects in the form of sand can be seen clearly. Objects found on the sunken object coordinates 1°02.5817 N and 104°08.7337 E with the highest gain value is 8 dB with altitude 16.5 m and data ping 3984. Linear regression has y = 0.5106x +12.84 with R2 = 0.5106 (51%). Track 1 has many targets identification results compared Track 2 and 3.
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34

Dura, E., J. Bell, and D. Lane. "Reconstruction of textured seafloors from side-scan sonar images." IEE Proceedings - Radar, Sonar and Navigation 151, no. 2 (2004): 114. http://dx.doi.org/10.1049/ip-rsn:20040262.

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35

Coiras, Enrique, Yvan Petillot, and David M. Lane. "Multiresolution 3-D Reconstruction From Side-Scan Sonar Images." IEEE Transactions on Image Processing 16, no. 2 (2007): 382–90. http://dx.doi.org/10.1109/tip.2006.888337.

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36

Fish, J. P., and M. Klein. "The Use of Reconnaissance Hydrographic Surveys for Establishment of Essential Navigation Bathymetric Data in the Third World Countries." Canadian Surveyor 39, no. 2 (1985): 143–45. http://dx.doi.org/10.1139/tcs-1985-0018.

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Many third world countries do not have the resources to conduct classic hydrographic surveys, either due to the length of the coastline or the level of available funds. In the past, this has resulted in an arbitrary choice of navigational channel areas to be surveyed. In order to more effectively utilize the limited resources available in third world countries, the use of a large footprint side scan sonar to provide reconnaissance type information to the surveyor is advocated so that the surveyor may “triage” the large areas to be surveyed and survey only areas which offer the highest return for the application of resources. The use of the large footprint side scan sonar to augment the traditional narrow beam echo sounder trace permits the use of wider spaced lines without undue risk of missing significant bottom obstructions. With increasing commerce to Third World countries, accurate bathymetry becomes increasingly significant. The utilization of selected systems such as the side scan sonar can permit the Third World hydrographer to maximize utilization of available resources to obtain this data. A complete discussion of the methodology involved with the utilization of side scan sonar, as well as the employment of the equipment, is discussed in this paper.
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37

Alekseev, G. G., E. A. Alekseeva, A. P. Sorokin, and S. A. Sorokin. "Complex of morphological analysis of images for side-scan sonar on basis of Griffon hardware platform." Issues of radio electronics 49, no. 9 (2020): 30–37. http://dx.doi.org/10.21778/2218-5453-2020-9-30-37.

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The article describes the developed computational complex of morphological image analysis for interferometric side-scan sonars. Examples of modeling the operation of the side-scan sonar in cases where the structure of bottom sediments is not known in advance are given. The results of the analysis of experimental data are considered. A description is given of the structure for constructing algorithms for morphological image analysis used in the operation of the complex. Recommendations are given on the effective use of the computing capabilities of the Griffon hardware platform for organizing parallel-conveyor data processing of side-scan interference sonars in real time. Hardware and software modeling has been performed to assess the most important performance characteristics of a computing complex and determine the optimal organization of data processing. The obtained results of processing sonar images streams showed that the use of the Griffon hardware platform provides advantages in terms of organizing the computing process, including parallel operation of different sections of the computing pipeline, reducing the load on the transport bus and pipeline delay, as well as reducing the load and freeing up CPU resources for morphological processing.
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38

Grothues, Thomas M., Arthur E. Newhall, James F. Lynch, Kaela S. Vogel, and Glen G. Gawarkiewicz. "High-frequency side-scan sonar fish reconnaissance by autonomous underwater vehicles." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 2 (2017): 240–55. http://dx.doi.org/10.1139/cjfas-2015-0301.

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A dichotomy between depth penetration and resolution as a function of sonar frequency, draw resolution, and beam spread challenges fish target classification from sonar. Moving high-frequency sources to depth using autonomous underwater vehicles (AUVs) mitigates this and also co-locates transducers with other AUV-mounted short-range sensors to allow a holistic approach to ecological surveys. This widely available tool with a pedigree for bottom mapping is not commonly applied to fish reconnaissance and requires the development of an interpretation of pelagic reflective features, revisitation of count methods, image-processing rather than wave-form recognition for automation, and an understanding of bias. In a series of AUV mission test cases, side-scan sonar (600 and 900 kHz) returns often resolved individual school members, spacing, size, behavior, and (infrequently) species from anatomical features and could be intuitively classified by ecologists — but also produced artifacts. Fish often followed the AUV and thus were videographed, but in doing so removed themselves from the sonar aperture. AUV-supported high-frequency side-scan holds particular promise for survey of scarce, large species or for synergistic investigation of predators and their prey because the spatial scale of observations may be similar to those of predators.
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Huang, Honghe, Zhen Zuo, Bei Sun, Peng Wu, and Jiaju Zhang. "DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation." Applied Sciences 12, no. 18 (2022): 9365. http://dx.doi.org/10.3390/app12189365.

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Side-scan sonar systems play an important role in tasks such as marine terrain exploration and underwater target identification. Target segmentation of side-scan sonar images is an effective method of underwater target detection. However, the principle of side-scan sonar systems leads to high noise interference, weak boundary information, and difficult target feature extraction of sonar images. To solve these problems, we propose a Double Split Attention (DSA) SOLO. Specially, we present an efficient attention module called DSA which fuses spatial attention and channel attention together effectively. DSA first splits feature maps into two parts along channel dimensions before processing them in parallel. Next, DSA utilizes C-S Unit and S-C Unit to describe relevant features in the spatial and channel dimensions, respectively. After that, the results of the two parts are aggregated to improve feature representation. We embedded the proposed DSA module after the FPN network of SOLOv2, and this approach improves the instance segmentation accuracy to a great extent. Experimental results show that our proposed DSA-SOLO on SCTD dataset achieves 78.4% mAP.5, which is 5.1% higher than SOLOv2.
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40

Zhao, Kun, Jisheng Ding, YanFei Sun, and ZhiYuan Hu. "Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means." E3S Web of Conferences 206 (2020): 03019. http://dx.doi.org/10.1051/e3sconf/202020603019.

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In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.
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41

Ruan, Fengxue, Lanxue Dang, Qiang Ge, Qian Zhang, Baojun Qiao, and Xianyu Zuo. "Dual-Path Residual “Shrinkage” Network for Side-Scan Sonar Image Classification." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/6962838.

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The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater environment. At present, the main seabed detection technology explores the seabed environment with sonar equipment. However, the characteristics of underwater sonar imaging (e.g., low contrast, blurred edges, poor texture, and unsatisfactory quality) have serious negative influences on such image classification. Therefore, in this study, we propose a dual-path deep residual “shrinkage” network (DP-DRSN) module, which is a simple and effective neural network attention module that can classify side-scan sonar images. Specifically, the module can extract background and feature texture information of the input feature mapping through different scales (e.g., global average pooling and global max pooling), whereas scale information passes through a two-layer 1 × 1 convolution to increase nonlinearity. This helps realize cross-channel information interaction and information integration simultaneously before outputting threshold parameters in a sigmoid layer. The parameters are then multiplied by the average value of the input feature mapping to obtain a threshold, which is used to denoise the image features using the soft threshold function. The proposed DP-DRSN study provided higher classification accuracy and efficiency than other models. In this way, the feasibility and effectiveness of DP-DRSN in image classification of side-scan sonar are proven.
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42

Tao, Weiliang, Yan Liu, and Wenbin Hu. "Inversion of Side Scan Sonar Motion and Posture in Seabed Geomorphology." Polish Maritime Research 24, s2 (2017): 81–88. http://dx.doi.org/10.1515/pomr-2017-0068.

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Abstract Side scan sonar measurement platform, affected by underwater environment and its own motion precision, inevitably has posture and motion disturbance, which greatly affects accuracy of geomorphic image formation. It is difficult to sensitively and accurately capture these underwater disturbances by relying on auxiliary navigation devices. In this paper, we propose a method to invert motion and posture information of the measurement platform by using the matching relation between the strip images. The inversion algorithm is the key link in the image mosaic frame of side scan sonar, and the acquired motion posture information can effectively improve seabed topography and plotting accuracy and stability. In this paper, we first analyze influence of platform motion and posture on side scan sonar mapping, and establish the correlation model between motion, posture information and strip image matching information. Then, based on the model, a reverse neural network is established. Based on input, output of neural network, design of and test data set, a motion posture inversion mechanism based on strip image matching information is established. Accuracy and validity of the algorithm are verified by the experimental results.
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43

Lubis, Muhammad Zainuddin, Kasih Anggraini, Husnul Kausarian, and Sri Pujiyati. "Review: Marine Seismic And Side-Scan Sonar Investigations For Seabed Identification With Sonar System." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 2 (2017): 166. http://dx.doi.org/10.24273/jgeet.2017.2.2.253.

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Marine seismic reflection data have been collected for decades and since the mid-to late- 1980s much of this data is positioned relatively accurately. Marine geophysical acquisition of data is a very expensive process with the rates regularly ship through dozens of thousands of euros per day. Acquisition of seismic profiles has the position is determined by a DGPS system and navigation is performed by Hypack and Maxview software that also gives all the offsets for the equipment employed in the survey. Examples of some projects will be described in terms of the project goals and the geophysical equipment selected for each survey and specific geophysical systems according to with the scope of work. For amplitude side scan sonar image, and in the multi-frequency system, color, becoming a significant properties of the sea floor, the effect of which is a bully needs to be fixed. The main confounding effect is due to absorption of water; geometric spread; shape beam sonar function (combined transmit-receive sonar beam intensity as a function of tilt angle obtained in this sonar reference frame); sonar vehicle roll; form and function of the seabed backscatter (proportion incident on the seabed backscattered signal to sonar as a function of the angle of incidence relative to the sea floor); and the slope of the seabed. The different angles of view are generated by the translation of the sonar, because of the discrete steps involved by the sequential pings, the angular sampling of the bottom.
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44

Arriafdi, N., O. Zainon, U. Din, et al. "HULU SUNGAI PERAK BED SEDIMENT MAPPING USING UNDERWATER ACOUSTIC SONAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 30, 2016): 339–43. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-339-2016.

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Development in acoustic survey techniques in particular side scan sonar have revolutionized the way we are able to image, map and understand the riverbed environment. It is now cost effective to image large areas of the riverbed using these techniques and the backscatter image created from surveys provides base line data from which thematic maps of the riverbed environment including maps of morphological geology, can be derived when interpreted in conjunction with in situ sampling data. This article focuses on investigation characteristics of sediments and correlation of side scan backscatter image with signal strength. The interpretation of acoustic backscatter rely on experienced interpretation by eye of grey scale images produced from the data. A 990F Starfish Side Scan Sonar was used to collect and develop a series of sonar images along 6 km of Hulu Sungai Perak. Background sediments could be delineated accurately and the image textures could be linked to the actual river floor appearance through grab sampling. A major difference was found in the acoustic returns from the two research area studies: the upstream area shows much rougher textures. This is due to an actual differences in riverbed roughness, caused by a difference in bottom currents and sediment dynamics in the two areas. The highest backscatter correlates with coarsest and roughness sediment. Result suggest that image based backscatter classification shows considerable promise for interpretation of side scan sonar data for the production of geological maps.
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45

Zhou, Yan, Qingwu Li, and Guanying Huo. "Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/493142.

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We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms.
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46

Yan, Jun, Junxia Meng, and Jianhu Zhao. "Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks." Remote Sensing 12, no. 1 (2019): 37. http://dx.doi.org/10.3390/rs12010037.

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As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.
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47

Sun, Yushan, Haotian Zheng, Guocheng Zhang, Jingfei Ren, Hao Xu, and Chao Xu. "DP-ViT: A Dual-Path Vision Transformer for Real-Time Sonar Target Detection." Remote Sensing 14, no. 22 (2022): 5807. http://dx.doi.org/10.3390/rs14225807.

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Sonar image is the main way for underwater vehicles to obtain environmental information. The task of target detection in sonar images can distinguish multi-class targets in real time and accurately locate them, providing perception information for the decision-making system of underwater vehicles. However, there are many challenges in sonar image target detection, such as many kinds of sonar, complex and serious noise interference in images, and less datasets. This paper proposes a sonar image target detection method based on Dual Path Vision Transformer Network (DP-VIT) to accurately detect targets in forward-look sonar and side-scan sonar. DP-ViT increases receptive field by adding multi-scale to patch embedding enhances learning ability of model feature extraction by using Dual Path Transformer Block, then introduces Conv-Attention to reduce model training parameters, and finally uses Generalized Focal Loss to solve the problem of imbalance between positive and negative samples. The experimental results show that the performance of this sonar target detection method is superior to other mainstream methods on both forward-look sonar dataset and side-scan sonar dataset, and it can also maintain good performance in the case of adding noise.
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48

O, Hyeong-Sun, Tae-Hyung Kim, Soon-Do Kwon, et al. "Applications of Side Scan Sonar for Shipbuilding and Offshore Project." Journal of Ocean Engineering and Technology 29, no. 5 (2015): 373–79. http://dx.doi.org/10.5574/ksoe.2015.29.5.373.

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49

Kim, Myung-Kyung, Da-Woon Lee, Ho-Seuk Bae, Yong-Jun You, and Woo-Keen Chung. "Analysis of operating conditions using side scan sonar modeling algorithm." Journal of the Korean Society of Marine Engineering 42, no. 6 (2018): 491–96. http://dx.doi.org/10.5916/jkosme.2018.42.6.491.

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50

Wu, Wang, Rigall, et al. "ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation." Sensors 19, no. 9 (2019): 2009. http://dx.doi.org/10.3390/s19092009.

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This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.
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