In order to realise the fast detection of ships in synthetic aperture radar (SAR) images, a detection method combining visual saliency and a cascade convolutional neural network (CNN) is proposed. First, based on visual saliency, a multiscale spectral residual model is designed for realising the fast detection of ship candidate regions. Then, a cascaded CNN is designed, which consists of two convolution networks, namely, the front-end shallow CNN, which is used to quickly exclude obvious non-ship candidates and classify the ship candidates according to the ship orientation, and the back-end deep CNN, which is used to detect high-probability candidate regions with rotatable boundary boxes. The whole structure can realise the fast detection and precise positioning of ships with an arbitrary orientation. Finally, the authors conduct detailed experiments on the SAR ship image dataset. The experimental results show that the proposed method can effectively improve the detection accuracy of ships, ensuring the detection efficiency in SAR images.

中文翻译：

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结合视觉显着性和SAR图像中级联CNN的快速船舶检测

为了实现合成孔径雷达图像中船舶的快速检测，提出了一种结合视觉显着性和级联卷积神经网络（CNN）的检测方法。首先，基于视觉显着性，设计了一种多尺度谱残差模型，用于实现舰船候选区域的快速检测。然后，设计了一个级联的CNN，该级联由两个卷积网络组成，即前端浅层CNN，用于快速排除明显的非舰船候选者并根据舰船方位对舰船候选者进行分类，而后排深度深CNN，用于检测具有可旋转边界框的高概率候选区域。整个结构可以实现任意方向的船舶快速检测和精确定位。最后，作者对SAR舰船图像数据集进行了详细的实验。实验结果表明，该方法可以有效提高舰船的探测精度，保证了SAR图像的探测效率。