Abstract
In the field of ship image recognition and classification, traditional algorithms lack attention to the differences between the grain of ship images. The differences in the hull structure of different categories of ships are reflected in the coarse-grain, whereas the differences in the ship equipment and superstructures of different ships of the same category are reflected in the fine-grain. To extract the ship features of different scales, the multi-scale paralleling CNN oriented on ships images (SMS-PCNN) model is proposed in this paper. This model has three characteristics. (1) Extracting image features of different sizes by parallelizing convolutional branches with different receptive fields. (2) The number of channels of the model is adjusted two times to extract features and eliminate redundant information. (3) The residual connection network is used to extend the network depth and mitigate the gradient disappearance. In this paper, we collected open-source images on the Internet to form an experimental dataset and conduct performance tests. The results show that the SMS-PCNN model proposed in this paper achieves 84.79% accuracy on the dataset, which is better than the existing four state-of-the-art approaches. By the ablation experiments, the effectiveness of the optimization tricks used in the model is verified.