OPD-Net: Prow Detection Based on Feature Enhancement and Improved Regression Model in Optical Remote Sensing Imagery

Abstract

Accurate prow detection (i.e., ship heading prediction) is important in many applications that rely on optical remote sensing imagery, such as track forecasting and maritime navigation. In recent years, many advanced methods based on deep convolution neural networks (DCNNs) have succeeded in detecting multidirectional ships. However, these methods are not effective at determining the prow orientation, primarily due to three limitations: weak adaptability to geometric transformations of ship targets, confusing the semantic information between the prow and other parts of ships, and the boundary discontinuity problem. To address these problems, we propose an omnidirectional prow detection network (OPD-Net) based on feature enhancement and an improved regression model. OPD-Net consists of a feature refinement network (FRN), a prow attention network (PAN), and a complex plane coordinates regression model (CPCRM). First, the FRN balances the low-level location information and high-level semantic information from multiscale feature maps and then fits various geometric transformations regarding ship targets through deformable blocks. Next, the PAN, which is based on supervised learning, is used to enhance the ship prow feature as well as suppress background noise, which improves the accuracy of ship heading predictions. Finally, the CPCRM is designed to effectively solve the boundary discontinuity problem and correctly achieve prow detection in arbitrary orientations. Experiments on optical remote sensing image data sets demonstrate the robustness and superiority of our method for prow detection. Moreover, our approach is also competitive when used only for ship detection.