基于多尺度卷积的船舶行为识别方法

针对复杂海洋环境下人工监管船舶行为效率低的问题，提出了一种基于多尺度卷积神经网络的船舶行为识别方法。首先，从船舶自动识别系统（AIS）中获取海量船舶行驶数据，并提取出具有判别力的船舶行为轨迹；然后，根据轨迹数据的特性，利用多尺度卷积设计并实现了针对船舶轨迹数据的行为识别网络，并且使用特征通道加权以及长短时记忆网络（LSTM）来提高算法的准确率。在船舶行为数据集上的实验结果表明，对于指定长度的船舶轨迹，所提识别网络能够达到92.1%的识别准确率，相较于传统的卷积神经网络提高了5.9个百分点，并且在稳定性以及收敛速度上都有明显提升。该方法能够有效地提高船舶行为的识别精度，为海洋监管部门提供高效的技术支持。

Ship behavior recognition method based on multi-scale convolution

The ship behavior recognition by human supervision in complex marine environment is inefficient. In order to solve the problem, a new ship behavior recognition method based on multi-scale convolutional neural network was proposed. Firstly, massive ship driving data were obtained from the Automatic Identification System (AIS), and the discriminative ship behavior trajectories were extracted. Secondly, according to the characteristics of the trajectory data, the behavior recognition network for ship trajectory data was designed and implemented by multi-scale convolution, and the feature channel weighting and Long Short-Term Memory network (LSTM) were used to improve the accuracy of algorithm. The experimental results on ship behavior dataset show that, the proposed recognition network can achieve 92.1% recognition accuracy for the ship trajectories with specific length, which is 5.9 percentage points higher than that of the traditional convolutional neural network. In addition, the stability and convergence speed of the proposed network are significantly improved. The proposed method can effectively improve the ship behavior recognition accuracy, and provide efficient technical support for the marine regulatory authority.