No abstract

Ship detection in remote sensing has been achieving increasing significance recently. In remote sensing, ships are arbitrary oriented and the detector has to learn the object features of arbitrary orientation by rote, which demands a large amount of training data to prevent overfitting. In addition, plenty of ships have a distinct direction from the center point to the head point. However, little attention has been paid to the direction information of ships and previous studies just predict the bow directions of ships. In this paper, we propose to further exploit the ship direction information to solve the arbitrary orientation problem, including direction augmentation, direction prediction, and direction normalization. A Variable-Direction Rotated RoI Align module is designed for direction augmentation and normalization with an additional feature extraction direction as input. The direction augmentation method directly augments the features of ship RRoIs and brings great diversities to the training data set. The direction prediction introduces additional direction information for learning and helps to reduce noise. In the direction normalization method, the predicted ship directions are utilized to normalize the directions of ship features from stern to bow through the VDR RoI Align module, making the ship features present in one orientation and easier to be identified by the detector. On the L1 task of the HRSC2016 data set, the direction augmentation method and direction normalization method boost the RoI Transformer baseline from 86.2% to 90.4% and 90.6%, respectively, achieving the state-of-the-art performance.

No abstract