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
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
Side-scan sonar data collected by Cruises 99-09 Leg 2 and 00-06 Leg 1 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 1 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
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
针对测深侧扫声呐进行波达方向(Direction of Arrival,DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续...针对测深侧扫声呐进行波达方向(Direction of Arrival,DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续线阵划分为多个子阵,并将各个子阵在预估计方向做加权波束形成;再次,采用加权子空间拟合(Weighted Subspace Fitting,WSF)算法构造代价函数;最后,采用阻尼牛顿法求解得到高精度的DOA估计结果。仿真结果表明,文中所提算法在阵元出现幅度相位误差条件下的角度估计均方误差相对于WSF算法减少了约0.03°。海试数据分析结果表明,文中所提算法的测深点均方误差整体优于WSF算法,其相对测深精度提高了约9.8个百分点。以上分析结果表明,文中所提算法整体优于WSF算法,可以实现在阵元幅度相位误差及低信噪比情况下的高精度DOA估计。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.
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.
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 1 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.
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.
文摘针对测深侧扫声呐进行波达方向(Direction of Arrival,DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续线阵划分为多个子阵,并将各个子阵在预估计方向做加权波束形成;再次,采用加权子空间拟合(Weighted Subspace Fitting,WSF)算法构造代价函数;最后,采用阻尼牛顿法求解得到高精度的DOA估计结果。仿真结果表明,文中所提算法在阵元出现幅度相位误差条件下的角度估计均方误差相对于WSF算法减少了约0.03°。海试数据分析结果表明,文中所提算法的测深点均方误差整体优于WSF算法,其相对测深精度提高了约9.8个百分点。以上分析结果表明,文中所提算法整体优于WSF算法,可以实现在阵元幅度相位误差及低信噪比情况下的高精度DOA估计。
