目的 遥感图像上任意方向舰船目标的检测，是给出舰船在图像上的最小外切矩形边界框。基于双阶段深度网络的任意方向舰船检测算法速度较慢；基于单阶段深度网络的任意方向舰船检测算法速度较快，但由于舰船具有较大长宽比的形态特点，导致虚警率较高。为了降低单阶段目标检测的虚警率，进一步提升检测速度，针对舰船目标的形态特点，提出了基于密集子区域切割的快速检测算法。方法 沿长轴方向，将舰船整体密集切割为若干个包含在正方形标注框内的局部子区域，确保标注框内最佳的子区域面积有效占比，保证核心检测网络的泛化能力；以子区域为检测目标，训练核心网络，在训练过程对重叠子区域进行整合；基于子图分割将检测得到的子区域进行合并，进而估计方向角度等关键舰船目标参数。其中采用子区域合并后处理替代了非极大值抑制后处理，保证了检测速度。结果 在HRSC2016（high resolution ship collections）实测数据集上，与最新的改进YOLOv3（you only look once）、RRCNN （rotated region convolutional neural network）、RRPN （rotation region proposal networks）、R-DFPN-3（rotation dense feature pyramid network）和R-DFPN-4等5种算法进行了比较，相较于检测精度最高的R-DFPN-4对照算法，本文算法的mAP （mean average precision）（IOU （inter section over union）=0.5）值提高了1.9%，平均耗时降低了57.9%；相较于检测速度最快的改进YOLOv3对照算法，本文算法的mAP （IOU=0.5）值提高了3.6%，平均耗时降低了31.4%。结论 本文所提出的任意方向舰船检测算法，结合了舰船目标的形态特点，在检测精度与检测速度均优于当前主流任意方向舰船检测算法，检测速度有明显提升。

Objective Ship detection based on remotely sensed images aims to locate ships, which is of great significance in national water surveillance and territorial security. The rectangular bounding boxes for target location in the typical deep learning method are usually in the horizontal-vertical direction, whereas the distribution of ships on remotely sensed images is arbitrarily oriented or in varying directions. For narrow and long ships with arbitrary directions, the vertical-horizontal bounding box is fairly rough. When the ship deviates from the vertical or horizontal direction, the bounding box is inaccurate, and the bounding box has many nonship pixels. If multiple ships are close to one another on the image, several ships may not be located because they are overlapped by the bounding boxes of the neighboring ships. Therefore, using a finer bounding box in detection is beneficial for detecting ship targets, and more precise ship positioning information is helpful for subsequent ship target recognition. For this reason, the classical deep-learning-based target detection is extended, and a finer minimum circumscribed rectangular bounding box is utilized to locate the ship target. Existing extended detection algorithms can be divided into two categories:one-stage detection and two-stage detection. One-stage detection directly outputs the target's location estimation, whereas two-stage detection classifies the proposed regions to eliminate the false targets. The disadvantage of two-stage detection is its slower speed. One-stage detection is faster, but its false alarm rate is higher for narrow and long ships. A fast detection algorithm based on dense sub-region segmentation is proposed according to the shape characteristics of ship targets to reduce the false alarm rate of one-stage detection and further improve the detection speed. Method The basic idea of our algorithm is to segment a ship into several sub-regions on which detection and combination are carried out, according to the long and narrow shape characteristic of ships. First, the whole ship is intensively segmented along its long axis direction into several local sub-regions contained in square annotation boxes to maximize the proportion of the pixel area belonging to the ship, namely, the effective area ratio in every annotation box.The influence of background noise on a sub-region annotation box could be suppressed, and the reliable generalization ability of the sub-regions detection network is obtained. The multi-resolution structure is applied to the core detection network that contains three output branches from coarse resolution to fine resolution. The density of sub-region segmentation is estimated according to the minimum spatial compression ratio of the output branches to ensure that the sub-regions of the same ship are connected in each output branch. Second, the core sub-region detection network is trained, and several overlapping sub-regions in the coarse branches are reorganized during training. In the output layer with a finer resolution, the spatially adjacent sub-regions may be mapped to the same point in the output grid because the sub-regions are densely distributed. This process is called sub-region overlapping. Each point in the output grid can only correspond to one sub-region target at most; thus, these sub-regions should be reorganized into a new pseudo sub-region. The center point of the pseudo sub-region is the average value of the center points of the original sub-regions, and the size of the pseudo sub-region is consistent with that of the original sub-regions. With different resolutions in the output layer, the center points of the pseudo sub-region are slightly different, but the overall difference is not large. Lastly, the detected sub-regions are merged based on the subgraph segmentation method. The whole remotely sensed image is modeled as a graph, where each detected sub-region is recognized as a single node. The connectivity between every two sub-regions is constructed according to their spatial distance and size difference. The sub-graph segmentation is clustered into sub-regions belonging to the same ship. Based on the spatial distribution of the clustered sub-regions, the key parameters of the corresponding ship such as length, width, and rotation angle are estimated. Compared with conventional deep learning target detection methods, the core detection network structure of the proposed algorithm remains unchanged, and the post processing of sub-region merging replaces common non maximum suppression post processing. Result Our algorithm is compared with five state-of-the-art detection algorithms, namely, improved YOLOv3(you only look once), RRCNN(rotated region convolutional neural network), RRPN(rotation region proposal netwrok), R-DFPN-3(rotation dense feature pyramid network), and R-DFPN-4 on the HRSC2016(high resolution ship collections) dataset. The improved YOLOv3 belongs to one-stage detection, and the four other algorithms belong to two-stage detection. The quantitative evaluation metrics include mean average precision(mAP) and mean consuming time(mCT). Experiment results show that our algorithm outperforms all other algorithms on the HRSC2016 dataset. Compared with the result of R-DFPN-4 with the highest detection accuracy in comparison algorithms, mAP (i.e., higher is better) increases by 1.9%, and mCT(i.e., less is better) decreases by 57.9%. Compared with the result of improved YOLOv3 with the fastest detection speed in comparison algorithms, mAP increases by 3.6%, and mCT decreases by 31.4%. The running speed of our algorithm and the conventional YOLOv3 algorithm are further analyzed and compared. The core detection network applied to our algorithm is the same as that of the conventional YOLOv3 algorithm; thus, the running speed differs only in the post processing phase. The sub-region merging of our algorithm takes about 11 ms, and the nom-maximum-suppression(NMS) of conventional YOLOv3 takes approximately 5 ms on the HRSC2016 dataset. Compared with the conventional YOLOv3 algorithm, our algorithm can obtain finer positioning information for the rotating ships, and running time increases by only 9%. Conclusion A dense sub-region segmentation-based, arbitrarily oriented ship detection algorithm by using the long-and-narrow shape characteristics of the ship target is proposed. The experiment results show that our algorithm outperforms several state-of-the-art arbitrary-oriented ship detection algorithms, especially in detection speed.