基于深度学习的船舶吃水线提取策略  

Ship waterline extraction strategy based on deep learning

作　　者：李雅倩[1] 薛银涛 李海滨[1] 张文明[1] 高雅昆 LI Ya-qian;XUE Yin-tao;LI Hai-bin;ZHANG Wen-ming;GAO Ya-kun(School of Electrical Engineering,Yanshan University,Qinhuangdao Hebei 066004,China)

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004

出　　处：《控制理论与应用》2020年第11期2347-2353,共7页Control Theory & Applications

基　　金：河北省人才工程培养项目(A201903005)资助.

摘　　要：提出了一种基于深度神经网络的船舶吃水线检测方法.相比传统手工设定的特征,基于深度神经网络的方法学习得到的特征具有较强的鲁棒性和稳定性,能够适应训练集中未曾出现的新物体.本方法首先使用基于深度学习的语义分割算法对图像中目标区域进行分割,通过水平投影得到水线在图像中的位置,然后根据统计方法得到最终的吃水深度.通过实验表明所提方法能对图像中的目标区域进行较为准确的分割,进而提取到水线值,通过与人工获取的结果对比,证明了所提方法的有效性.A method for detecting the position of ship draught based on deep neural network is proposed.Compared with the traditionally selected features,the features learned by the deep neural network-based method have strong robustness and stability,and can adapt to new objects that have not appeared in the training set.The method firstly uses the semantic segmentation algorithm based on deep learning to segment the target region in the image,and obtains the position of the waterline in the image through horizontal projection,and then obtains the final draft depth according to the statistical method.Experiments show that the proposed method can accurately segment the target region in the image,and then calculate the waterline value.By comparing with the results obtained by manual,and the results are proved to be effective.

关 键 词：船舶吃水线检测 深度学习 语义分割 

分 类 号：U693.4[交通运输工程—港口、海岸及近海工程] TP18[交通运输工程—船舶与海洋工程] TP391.41[自动化与计算机技术—控制理论与控制工程]