Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging alo...Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.展开更多
Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides ...Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.展开更多
TM Field,located offshore Niger Delta in the Gulf of Guinea(Nigeria),has been evaluated for potential drilling hazards prior to the drilling of a proposed oil well.This is in a region where potential hazards that coul...TM Field,located offshore Niger Delta in the Gulf of Guinea(Nigeria),has been evaluated for potential drilling hazards prior to the drilling of a proposed oil well.This is in a region where potential hazards that could inhibit successful drilling exercise have not been adequately studied.The study adopted the modern technique of offshore geohazard evaluation that relies mainly on suites of geophysical and shallow geological investigations which include ocean bathymetry using multi-beam echo sounder,sidescan sonar,sub-bottom profiler,magnetic,and 2D-high resolution seismic reflection surveys.The results were integrated to draw inferences about the risk potential of the field.From the bathymetric survey,water depths were found to range from 345 m to 650 m LAT,with seabed relief being 305 m.Water depth at the proposed well location was found to be approximately 450 m and the seabed was found to slope in the northwestern direction.Seafloor gradient was computed as 0.05 across the whole area.Two shallow sub-surface stratigraphic units,labelled A and B,were delineated.Unit A was directly beneath the seafloor and seemed to be composed mainly of clayey/silty sand.Its thickness ranged from 6 m to 70 m across the study area.Unit B appeared to be a layer of sand and its total thickness was not fully delineated.Observable kinks and displacements at the flanks of seabed undulations/depressions on subbottom profiler sections suggested that the depressions may be fault-controlled.On the map generated from the marine magnetic data,a magnetic fault was delineated and this appeared to be coincident with the shallower of the two buried faults on the cross-line seismic section cutting through the proposed well-head location.Interpreted seismic reflection data presented this fault as being inactive.Most of the faults mapped,especially the buried ones,appeared to be inactive except a few.Regions with mapped amplitude anomalies are to be avoided during rig-mobilization operations.Though engineering conditions are expected to vary verti展开更多
In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS...In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.展开更多
This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,duri...This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,during the Second World War.This UXO survey was carried out between 2015-2018 by the Romanian Navy’s hydrographic ship“Commander Alexandru Cătuneanu”and Romanian Mine Warfare Data Center,using towed side-scan sonar technology and oceanographic observations.After explaining the materials and methodology,the results are presented and discussed:mosaics of the minefields,side-scan images of UXO contacts,side-scan images of the wrecks that were sunk in the minefields and some visible natural geological features of the seafloor.It was concluded that most of the objects discovered are sinkers,wreck debris or parts of chains,which does not represent a danger to navigation.展开更多
Multi-beam Sonar and Side-scan Sonar compensate each other. In order to fully utilize all information, it is necessary to fuse two kinds of image and data. And the image co-registration is an important and complicated...Multi-beam Sonar and Side-scan Sonar compensate each other. In order to fully utilize all information, it is necessary to fuse two kinds of image and data. And the image co-registration is an important and complicated job before fusion. This paper suggests combining bathymetric data with intensity image, obtaining the characteristic points through the minimal angles of lines, and then deciding the corresponding image points by the maximal correlate coefficient in searching space. Finally, the second order polynomial is applied to the deformation model. After the images have been co-registered, Wavelet is used to fuse the images. It is shown that this algorithm can be used in the flat seafloor or the isotropic seabed. Verification is made in the paper with the observed data.展开更多
Side-scan sonar data collected by Cruises 99-09 Leg 2 and 00-06 Leg l of R/V Yokosuka were used to reveal the sedimentary processes in Zenisu deep-sea channel. The middle and lower segments of the channel are rich in ...Side-scan sonar data collected by Cruises 99-09 Leg 2 and 00-06 Leg l of R/V Yokosuka were used to reveal the sedimentary processes in Zenisu deep-sea channel. The middle and lower segments of the channel are rich in turbidite and other debrite deposits. By high-resolution imaging, three sedimentary processes were distinguished with distinct acoustic features. 1. Slumps and slides occur with contrasting backscatter, rough surface textures, blockings, and acoustic shadows at headwalls. They are very extensive and often in lobate form downslope. 2. Debris flow has uniform, general medium backscatter, sometimes showing marbling/lineation in lobate form. 3. Turbidity current is characterized by low backscatter confined to the channel as acoustic signal is attenuated. Regional tectonics must be the dominating factor that controls deposition pattern in this area.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFC3010803)the National Nature Science Foundation of China(Grant No.52272424)+1 种基金the Key R&D Program of Hubei Province of China(Grant No.2023BCB123)the Fundamental Research Funds for the Central Universities(Grant No.WUT:2023IVB079)。
文摘Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.
基金funded by the Natural Science Foundation of Fujian Province(No.2018J01063)the Project of Deep Learning Based Underwater Cultural Relics Recognization(No.38360041)the Project of the State Administration of Cultural Relics(No.2018300).
文摘Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.
文摘TM Field,located offshore Niger Delta in the Gulf of Guinea(Nigeria),has been evaluated for potential drilling hazards prior to the drilling of a proposed oil well.This is in a region where potential hazards that could inhibit successful drilling exercise have not been adequately studied.The study adopted the modern technique of offshore geohazard evaluation that relies mainly on suites of geophysical and shallow geological investigations which include ocean bathymetry using multi-beam echo sounder,sidescan sonar,sub-bottom profiler,magnetic,and 2D-high resolution seismic reflection surveys.The results were integrated to draw inferences about the risk potential of the field.From the bathymetric survey,water depths were found to range from 345 m to 650 m LAT,with seabed relief being 305 m.Water depth at the proposed well location was found to be approximately 450 m and the seabed was found to slope in the northwestern direction.Seafloor gradient was computed as 0.05 across the whole area.Two shallow sub-surface stratigraphic units,labelled A and B,were delineated.Unit A was directly beneath the seafloor and seemed to be composed mainly of clayey/silty sand.Its thickness ranged from 6 m to 70 m across the study area.Unit B appeared to be a layer of sand and its total thickness was not fully delineated.Observable kinks and displacements at the flanks of seabed undulations/depressions on subbottom profiler sections suggested that the depressions may be fault-controlled.On the map generated from the marine magnetic data,a magnetic fault was delineated and this appeared to be coincident with the shallower of the two buried faults on the cross-line seismic section cutting through the proposed well-head location.Interpreted seismic reflection data presented this fault as being inactive.Most of the faults mapped,especially the buried ones,appeared to be inactive except a few.Regions with mapped amplitude anomalies are to be avoided during rig-mobilization operations.Though engineering conditions are expected to vary verti
基金This work is partially supported by the Natural Key Research and Development Program of China(No.2016YF C0301400).
文摘In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.
文摘This paper aims to provide the reader with the results of the Unexploded Ordnance(UXO)survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast,during the Second World War.This UXO survey was carried out between 2015-2018 by the Romanian Navy’s hydrographic ship“Commander Alexandru Cătuneanu”and Romanian Mine Warfare Data Center,using towed side-scan sonar technology and oceanographic observations.After explaining the materials and methodology,the results are presented and discussed:mosaics of the minefields,side-scan images of UXO contacts,side-scan images of the wrecks that were sunk in the minefields and some visible natural geological features of the seafloor.It was concluded that most of the objects discovered are sinkers,wreck debris or parts of chains,which does not represent a danger to navigation.
文摘Multi-beam Sonar and Side-scan Sonar compensate each other. In order to fully utilize all information, it is necessary to fuse two kinds of image and data. And the image co-registration is an important and complicated job before fusion. This paper suggests combining bathymetric data with intensity image, obtaining the characteristic points through the minimal angles of lines, and then deciding the corresponding image points by the maximal correlate coefficient in searching space. Finally, the second order polynomial is applied to the deformation model. After the images have been co-registered, Wavelet is used to fuse the images. It is shown that this algorithm can be used in the flat seafloor or the isotropic seabed. Verification is made in the paper with the observed data.
基金Financially supported by the NSFC (Grant No.40276022), KnowledgeInnovation Program of Chinese Academy of Sciences (KZCX3-SW-219)and JSPS international cooperation program, and the Ministry of Scienceand Technology Project (G200046704)
文摘Side-scan sonar data collected by Cruises 99-09 Leg 2 and 00-06 Leg l of R/V Yokosuka were used to reveal the sedimentary processes in Zenisu deep-sea channel. The middle and lower segments of the channel are rich in turbidite and other debrite deposits. By high-resolution imaging, three sedimentary processes were distinguished with distinct acoustic features. 1. Slumps and slides occur with contrasting backscatter, rough surface textures, blockings, and acoustic shadows at headwalls. They are very extensive and often in lobate form downslope. 2. Debris flow has uniform, general medium backscatter, sometimes showing marbling/lineation in lobate form. 3. Turbidity current is characterized by low backscatter confined to the channel as acoustic signal is attenuated. Regional tectonics must be the dominating factor that controls deposition pattern in this area.
