Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multiscale rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS and DOTA dataset for rotation detection shows that our detection method has a competitive performance.

该论文提出了一种基于多任务旋转区域卷积神经网络的全新检测模型，通过密集特征金字塔网络、自适应兴趣区域对齐、旋转边界框回归、驶向预测和旋转非极大值抑制等五个连续部分解决了应用场景的复杂性、检测区域的冗余性和密集船只检测的困难等主要障碍。在旋转检测的基础上，通过预测可以找到船只的泊位和驶向，并通过SRSS进行了详细的评估，证明该检测方法具有竞争性的性能。

多任务旋转区域卷积神经网络用于任意方向船舶的位置检测和方向预测