基于改进1DCNN-SAGRU模型的渔船作业方式识别

Recognition of Fishing Vessel Operation Mode Based on Improved 1DCNN-SAGRU Model

海洋渔业领域中渔船轨迹数据具有时空性和非平稳性的特点,针对目前渔船作业方式识别方法存在对数据信息提取不充分及识别精度低的问题,提出了一种基于一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)和加入自注意力(self-attention)的门控循环单元网络(gated recurrent unit,GRU)的渔船作业方式识别模型(1DCNN-SAGRU).模型利用一维CNN和GRU充分提取渔船轨迹数据的局部空间特征和时序上的依赖关系,并引入自注意力机制强化模型对关键信息的关注能力.最后引入dropout方法和RAdam优化器对模型进行改进和优化,防止模型过拟合的同时加快网络的收敛速度和输出准确性.经实验和分析表明,相较于其他对比模型,该模型在准确率上最高可提升4.4个百分点,说明该模型能更准确地识别渔船拖网、围网和刺网作业,有利于加强渔船监管能力和渔业资源的保护.

The trajectories of fishing vessels in the field of marine fisheries are spatiotemporal and non-stationary.Considering the problems of insufficient data extraction and low recognition accuracy in the current operation mode recognition methods for fishing vessels,an operation mode recognition model for fishing vessels,i.e.,1DCNN-SAGRU,is proposed.This model is based on the one-dimensional convolutional neural network(1DCNN)and the gated recurrent unit(GRU)network with self-attention.The model uses 1DCNN and GRU to fully extract local spatial features and temporal dependencies of the trajectory data of fishing vessels.In addition,the self-attention mechanism is introduced to strengthen the model’s ability to focus on key information.Finally,the dropout method and the RAdam optimizer are introduced to improve and optimize the model,which can prevent the overfitting of the model,speed up the convergence,and raise the output accuracy of the network.Experiments and analysis show that compared with the accuracy of other comparative models,the accuracy of this model can be improved by up to 4.4 percentage points.This indicates that the model can more accurately identify the trawl,purse seine,and gill net operations of fishing vessels,which is conducive to strengthening the regulatory capacity of fishing vessels and the protection of fishery resources.