Ship classification based on convolutional neural networks

ABSTRACT

The main bottleneck limiting the use of traditional ship classification methods is the manual extraction of ship images before classification. To solve this problem, a ship classification method based on a convolutional neural network (CNN) is proposed in this paper. A CNN model can autonomously extract image features, avoiding complex feature selection and extraction processes. In view of the problem of an insufficient number of ship samples, transfer learning was applied to train the model using the ImageNet dataset, effectively alleviating the over-fitting phenomenon in the training process. Experiments showed that the CNN model had an accuracy of 98% in ship classification using the SHIP-3 dataset. The CNN was robust to external environmental challenges – such as illumination – the accuracy of ship classification in foggy and night-time conditions reaching 75%, greatly exceeding the performance of traditional machine learning algorithms.

KEYWORDS:

• Ship classification
• convolutional neural network (CNN)
• support-vector machine (SVM)
• transfer learning
• environmental factors

Disclosure statement

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Additional information

Funding

This research was financially supported by the National Science Foundation of China (grant number 51979036).