Improvement of rotated object detection and instance segmentation in warship satellite remote sensing images based on convolutional neural network

Object detection and instance segmentation networks are improved to realise the accurate detection and instance segmentation of rotated warship objects in satellite remote sensing images. An adaptive threshold generation scheme and segmentation annotation information are applied used to improve a rotated label generation method to obtain high-precision rotated object labels. The original RPN is combined with the bbox head with improved output dimensions to obtain a rotated RPN to generate rotated region proposals. Rotated RoIAlign is used to solve the problem of mismatch between rotated region proposals and dimensions of subsequent feature maps. A rotated detection frame is used to correct the output of the network, which alleviates false detection and omission. In addition, this removes the pixels outside the rotated detection frame that are incorrectly classified as objects. The improved networks can achieve high-precision detection and instance segmentation of rotated warship objects, and the methods used in this study have good generalisability.