Department of Computer Science and Information Technology, Hefei University of Technology, Hefei 230000, China.
Department of Electronic Information Technology and Electric Engineering, Hefei University, Hefei 230000, China.
Sensors (Basel). 2018 Aug 29;18(9):2851. doi: 10.3390/s18092851.
Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.
在 SAR 图像中进行船舶检测和角度估计在海洋监测中起着重要作用。以前的工作首先检测船舶，然后估计其方向。这既耗时又乏味。为了解决上述问题，我们尝试使用卷积神经网络将这两个任务结合起来，以便同时检测船舶并估计其方向。所提出的方法基于原始 SSD（单发检测器），但使用可旋转的边界框。该方法仅通过一次正向计算即可学习和预测船舶的类别、位置和角度信息。生成的定向边界框比传统边界框紧密得多，对背景干扰具有鲁棒性。我们开发了一种语义聚合方法，该方法以自顶向下的方式融合特征。该方法可以提供丰富的位置和语义信息，有助于分类和定位。我们在六个预测层中采用了注意力模块。它可以自适应地选择有意义的特征并忽略较弱的特征。这有助于检测小船。设计了具有不同大小、纵横比和方向的多方向锚点，这可以兼顾速度和准确性。角度回归被嵌入到现有的边界框回归模块中，因此可以在输出位置和得分的同时输出角度预测，而不需要太多额外的计算。带有角度回归的损失函数用于优化模型。使用平均角度精度（AAP）来评估性能。在数据集上的实验证明了我们方法的有效性。
