Ship and course detection in remote sensing images based on key-point extraction

Remote sensing image ship target detection and course discrimination is one of the important supports for building a maritime power. Since ship target in remote sensing images are generally in strips, the IOU score is very sensitive to the angle of bounding box. Moreover, the angle of the ship is a periodic function, this discontinuity will cause performance degeneration. Meanwhile, methods generally use oriented bounding boxes as anchors to handle rotated ship target and thus introduce excessive hyper-parameters such as box size, aspect ratios. Aiming at the problem of complex calculation of anchor frame traversal mechanism and discontinuity of angle regression caused by increasing angle attribute in ship target detection of remote sensing image, a ship target heading detection method based on ship head point is proposed. The discontinuous angle regression problem is transformed into a continuous key point estimation problem, and the ship target detection and heading recognition are unified. Second, CA attention mechanism is added to the feature extraction network to enhance the attention to the ship target and predict the center point of the ship target. The offset and target width at the center point are regressed. Then, return the heading point and offset to obtain the accurate heading point position. Next, the rotation angle of the ship is determined according to the coordinates of the center point and the ship head point. Combined with the predicted width and height of the ship, the rotation frame detection of the ship target is completed. Finally, the center point and the bow point are connected to determine the course of the ship target. The effectiveness of the proposed method is verified on the RFUE and open source HRSC2016 datasets, respectively, and it also has good robustness in complex environments.