Objective

2

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

2

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

2

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

2

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.