In shipping,which is one of the drivers of the world’s economy,many marine accidents continue to occur,such as ship collisions and grounding.To reduce marine collision accidents,seafarers’skills must be improved thr...In shipping,which is one of the drivers of the world’s economy,many marine accidents continue to occur,such as ship collisions and grounding.To reduce marine collision accidents,seafarers’skills must be improved through training.Therefore,the authors propose a ship handling training for collision avoidance(hereinafter referred to as“T for CA”)in which a group of several people discusses the ship handling for collision avoidance,assuming the situation of the collision avoidance.After T for CA implementation,anchoring training was done and the effect of T for CA was verified through comparison with a group where T for CA was not applied.Two instructors evaluated the anchoring training conducted with and without“T for CA”.The anchoring training experiment showed a difference of 27.5%in the achievement rate between the proposed training and previous training.T for CA maximises the effects of group work and resulted in good evaluations in the anchoring training experiments.The training was effective because the students themselves set the scenarios and devised ship handling strategies for collision avoidance.In addition,group work discussions helped deepen students’knowledge and skills.展开更多
锚框结构的舰船目标检测算法存在预设锚框与真实目标框难以精准匹配的问题,设计了一种基于合成孔径雷达(Synthetic Aperture Radar,SAR)图像的无锚框实时舰船目标检测算法。该算法以YOLOX-Nano(You Only Look Once X-Nano)框架为基础,...锚框结构的舰船目标检测算法存在预设锚框与真实目标框难以精准匹配的问题,设计了一种基于合成孔径雷达(Synthetic Aperture Radar,SAR)图像的无锚框实时舰船目标检测算法。该算法以YOLOX-Nano(You Only Look Once X-Nano)框架为基础,在骨干网络单元嵌入改进Ghost模块和挤压激励(Squeeze and Excitation,SE)模块。路径聚合网络(Path Aggregation Network,PANet)与改进Ghost模块和自适应空间特征融合(Adaptively Spatial Feature Fusion,ASFF)模块集成后提高了模型的特征表达能力。以输入图像分辨率为320×320像素为基准,相较于单发多框检测器(Single Shot MultiBox Detector,SSD)和YOLOv3-tiny(You Only Look Once v3-tiny)模型,实验结果显示本文算法在合成孔径雷达舰船检测数据集(SAR Ship Detection Dataset,SSDD)上平均正确率达到94.5%,参数量为0.87×10^(6),浮点计算量为0.61×10^(9),能够实现高精度和低复杂度的SAR图像舰船目标检测。展开更多
It is very common that a submarine pipeline has to pass through a ship mooring area near a harbor zone in the Bohai Bay, China. The risk assessment of accidental events induced by the potential anchoring ships is carr...It is very common that a submarine pipeline has to pass through a ship mooring area near a harbor zone in the Bohai Bay, China. The risk assessment of accidental events induced by the potential anchoring ships is carried out, which will lead to external interference with the pipeline. A procedure to calculate the probability for the anchoring activity in the ship mooring area to damage the underlying pipeline is proposed. The adopted methodology is based on the recommendations suggested by the DNV Codes. The same philosophy is also applied to estimate the damage probability that is concerned with sinking ships.展开更多
文摘In shipping,which is one of the drivers of the world’s economy,many marine accidents continue to occur,such as ship collisions and grounding.To reduce marine collision accidents,seafarers’skills must be improved through training.Therefore,the authors propose a ship handling training for collision avoidance(hereinafter referred to as“T for CA”)in which a group of several people discusses the ship handling for collision avoidance,assuming the situation of the collision avoidance.After T for CA implementation,anchoring training was done and the effect of T for CA was verified through comparison with a group where T for CA was not applied.Two instructors evaluated the anchoring training conducted with and without“T for CA”.The anchoring training experiment showed a difference of 27.5%in the achievement rate between the proposed training and previous training.T for CA maximises the effects of group work and resulted in good evaluations in the anchoring training experiments.The training was effective because the students themselves set the scenarios and devised ship handling strategies for collision avoidance.In addition,group work discussions helped deepen students’knowledge and skills.
文摘锚框结构的舰船目标检测算法存在预设锚框与真实目标框难以精准匹配的问题,设计了一种基于合成孔径雷达(Synthetic Aperture Radar,SAR)图像的无锚框实时舰船目标检测算法。该算法以YOLOX-Nano(You Only Look Once X-Nano)框架为基础,在骨干网络单元嵌入改进Ghost模块和挤压激励(Squeeze and Excitation,SE)模块。路径聚合网络(Path Aggregation Network,PANet)与改进Ghost模块和自适应空间特征融合(Adaptively Spatial Feature Fusion,ASFF)模块集成后提高了模型的特征表达能力。以输入图像分辨率为320×320像素为基准,相较于单发多框检测器(Single Shot MultiBox Detector,SSD)和YOLOv3-tiny(You Only Look Once v3-tiny)模型,实验结果显示本文算法在合成孔径雷达舰船检测数据集(SAR Ship Detection Dataset,SSDD)上平均正确率达到94.5%,参数量为0.87×10^(6),浮点计算量为0.61×10^(9),能够实现高精度和低复杂度的SAR图像舰船目标检测。
基金financially supported by the National Natural Science Foundation of China(Grant Nos.51322904 and 41272323)the National Basic Research Program of China(973 ProgramGrant No.2014CB046802)
文摘It is very common that a submarine pipeline has to pass through a ship mooring area near a harbor zone in the Bohai Bay, China. The risk assessment of accidental events induced by the potential anchoring ships is carried out, which will lead to external interference with the pipeline. A procedure to calculate the probability for the anchoring activity in the ship mooring area to damage the underlying pipeline is proposed. The adopted methodology is based on the recommendations suggested by the DNV Codes. The same philosophy is also applied to estimate the damage probability that is concerned with sinking ships.
