| Summary: | Aiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN). Firstly, the ResNet50 network is selected for automatic extraction of features, and the feature map is automatically provided for the improved R-FCN. Secondly, the R-FCN is improved according to the characteristics of the ship identification, which allows the R-FCN to fully perform its performance on ship detection. Finally, according to the problem that the recognition rate of small ships in some categories is small, on the first step, the method of Maxpooling increases the recognition rate of small ships by 4.08 percentage points; on the second step, the improvement of ROIAlign makes the improved R-FCN in this paper perform much better on small target ship identification than original R-FCN, and the recognition rate is increased by 13 percentage points totally. This paper is also compared with the current mainstream target detection algorithms such as Faster-RCNN. Experimental results show that the method has higher recognition accuracy and the rate is basically the same as other methods.
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