基于MCNN-GRU的舰面目标碰撞预警方法

Ship Surface Target Collision Warning Method Based on MCNN-GRU

为提升甲板舰面目标的转运安全性,提出一种多个CNN-GRU(multipleCNN-GRU,MCNN-GRU)碰撞预警网络模型。该网络融合了卷积神经网络(convolutional neural network,CNN)对单时间步信息特征的提取能力以及门控循环单元(gate recurrent unit,GRU)对时序序列的记忆能力,通过多通道网络结构提升对多时间步信息特征的处理性能;在数据集上,利用目标检测网络和关键点检测网络、位姿解算模型及碰撞检测方法制作舰面目标碰撞预警数据集。通过不同网络在数据集上进行实验的结果表明:该模型对舰面目标的双机碰撞预警精度为92.44%,具有较好的效果。

A multiple CNN-GRU(MCNN-GRU) collision warning network model is proposed to improve the transfer safety of ship surface targets.The network combines the feature extraction ability of convolutional neural network(CNN)for single time step information and the memory ability of gated recurrent unit(GRU) for time sequence.Multi-channel network structure is used to improve the processing performance of multi-time step information features.Target detection network,key point detection network,pose calculation model and collision detection method are used to produce ship surface target collision warning data set.The experimental results on different network data sets show that the collision warning accuracy of the model is 92.44%,and it has a good effect.