Abstract:

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.

Key words: deep learning, target detection, regional fully convolutional networks (R-FCN)

摘要：

针对复杂海情下需要对不同大小及种类的船舶进行检测的问题，提出一种基于深度学习的船舶检测方法，该方法主要针对区域全卷积网络（R-FCN）进行改进。首先选取ResNet50网络用于自动提取特征，并将Feature Map自动提供给改进的R-FCN；其次根据船舶识别的特性改进R-FCN，使得R-FCN在船舶检测上能够完全发挥其性能；最后根据部分类别船舶体积较小识别率低的问题，先采取最大池化层（Maxpooling）进行   改进，将小目标船舶识别率提高了4.08个百分点，之后针对ROIAlign进行改进。改进的R-FCN方法比原始的R-FCN在小目标船舶识别方面表现更优，精度共提升了13个百分点，还与目前主流的目标检测算法如Faster-RCNN等进行了对比。实验结果表明，该方法识别精度更高，速率与其他方法基本持平。

关键词: 深度学习, 目标检测, 区域全卷积网络（R-FCN）