Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the t...Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.展开更多
To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of...To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount.To compensate for the IL,two methods termed as intensity compensation for the PSBF（IC-PSBF） and the hybrid beamforming（HBF）,respectively,are proposed.The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs;and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer（SSBF） to the side beam region,respectively.Numerical simulations demonstrate the effectiveness of the two proposed methods.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
To protect the sustainability of the benefits from seas and near coastal areas,which have under the effect of the very complex hydrodynamic conditions and intensive human activities,without disrupting the balance of n...To protect the sustainability of the benefits from seas and near coastal areas,which have under the effect of the very complex hydrodynamic conditions and intensive human activities,without disrupting the balance of nature,it is necessary to image the status of the seafloor features.Therefore,this study presents the deformations,depositional conditions,underwater constructions,and the other non-natural impacts on the seafloor of the nearshore area at western Istanbul(between Küçükçekmece and Büyükçekmece lagoons)where it intensely used by the citizens.The results of the study may provide some guidance for understanding the impacts and risk factors of uses that are or will be conducted in coastal and/or near-coastal areas.Construction planning for civil coastal structures and areas should be done in great harmony with nature,minimizing negative environmental impacts.Although sediment distribution in the area is generally quite complex,the current state of the region,wave action,hydrodynamic conditions,the amount of material transported from the land,and bathymetry are important influencing factors.The seafloor has been damaged primarily by anchor deformation and associated bottom scanning,as well as disturbing trawl tracks.The seafloor was observed as partially shallowing near the constructions(such as natural gas pipelines,fishermen’s shelter,and port piles)of coastal areas and associated with sand deposits.Therefore,scanning the seafloor using side-scan sonar may provide valuable frequency data to prevent future disruptions.展开更多
基金supported by the National Key Research and Development Program of China(No.2016YFC0301400).
文摘Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.
基金supported by the National Natural Science Foundation of China(51509204)the Opening Project of State Key Laboratory of Acoustics(SKLA201501)the Fundamental Research Funds for the Central Universities(3102015ZY011)
文摘To reduce the computation burden of a large-aperture multiple-input multiple-output（MIMO） sonar imaging system,the phase-shift beamformer（PSBF） is used at the cost of bringing the intensity loss（IL）.The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount.To compensate for the IL,two methods termed as intensity compensation for the PSBF（IC-PSBF） and the hybrid beamforming（HBF）,respectively,are proposed.The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs;and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer（SSBF） to the side beam region,respectively.Numerical simulations demonstrate the effectiveness of the two proposed methods.
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。
文摘To protect the sustainability of the benefits from seas and near coastal areas,which have under the effect of the very complex hydrodynamic conditions and intensive human activities,without disrupting the balance of nature,it is necessary to image the status of the seafloor features.Therefore,this study presents the deformations,depositional conditions,underwater constructions,and the other non-natural impacts on the seafloor of the nearshore area at western Istanbul(between Küçükçekmece and Büyükçekmece lagoons)where it intensely used by the citizens.The results of the study may provide some guidance for understanding the impacts and risk factors of uses that are or will be conducted in coastal and/or near-coastal areas.Construction planning for civil coastal structures and areas should be done in great harmony with nature,minimizing negative environmental impacts.Although sediment distribution in the area is generally quite complex,the current state of the region,wave action,hydrodynamic conditions,the amount of material transported from the land,and bathymetry are important influencing factors.The seafloor has been damaged primarily by anchor deformation and associated bottom scanning,as well as disturbing trawl tracks.The seafloor was observed as partially shallowing near the constructions(such as natural gas pipelines,fishermen’s shelter,and port piles)of coastal areas and associated with sand deposits.Therefore,scanning the seafloor using side-scan sonar may provide valuable frequency data to prevent future disruptions.
