Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

中文翻译：

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基于粗到精级联卷积神经网络的船型识别

大多数先前的研究都是通过探索手工船舶特征来处理船舶类型识别的任务，这可能无法区分具有相似视觉外观的船舶。这种情况促使我们提出一种新颖的基于深度学习的船型识别框架，我们将其命名为从粗到细的级联卷积神经网络（CFCCNN）。首先，提出的 CFCCNN 框架对输入的训练船舶图像和数据进行格式化，并为 CFCCNN 的隐藏层提供可训练的输入数据。其次，粗略和精细步骤以嵌套方式运行，以探索不同船型的判别特征。更具体地说，粗步骤以与传统卷积神经网络类似的方式进行训练，而精细步骤引入了正则化机制以提取更多内在的船舶特征，并微调参数设置以获得更好的识别性能。最后，我们评估了 CFCCNN 模型在识别最常见类型的商船（油轮、集装箱、LNG 油轮、化学品船、杂货船、散货船等）方面的性能。实验结果表明，所提出的框架比传统的船型识别方法获得了更好的识别性能。