Ship Object Detection of Remote Sensing Images Based on Adaptive Rotation Region Proposal Network

Aiming at the problem that increased difficulties in detection of ship detection in remote sensing images caused by the narrow and long shape, disorderly distribution and other characteristics, a ship target detection method based on faster region-convolution neural network (Faster R-CNN) is proposed in this paper. The method uses a two-way network to extract ship target features. In order to make the feature map fully integrate the low-level detail information and high-level semantic information, a multi-scale fusion feature pyramid network (MFPN) is used for feature fusion; in the candidate frame generation stage, an adaptive rotation region proposal network (AR-RPN) is proposed to generate a rotating anchor frame at the center of the target to efficiently obtain highquality candidate frames. In order to improve the detection rate of the network to ship targets, the network is optimized with an improved loss function. The test results on the public ship data sets HRSC2016 and the ROTA show that the average accuracy of this method is 89.10% and 88.61%, respectively, which can well adapt to the shape and distribution characteristics of ships in remote sensing images.