T 2 Det: twin-tower detector with geometric invariance for oriented object detection

Oriented object detection (OOD) in remote sensing images (RSIs) is of increasing interest. Since RSIs often contain many oriented objects, it is valuable and challenging to discover geometric invariance of geospatial objects to improve the model’s perception of rotation angle and scale. In this paper, we propose a twin-tower detector (T2Det) for OOD in RSIs. Specifically, T2Det overcomes the challenges posed by the angles and scales of oriented object by developing a self-supervised (SS) branch that exploits geometric invariance based on the main branch. Then, we design a twin-tower (T2) loss function to enhance the network’s ability to perceive the geometric invariance of geospatial object, where a coarse loss function and a fine loss function are introduced for both branches to optimize the model from coarse to fine. In addition, T2 loss function optimization strategy based on global or refinement modes is developed to achieve the trade-off between the main branch and the SS branch. On three benchmark datasets, including VEDAI, HRSC2016, and NUAA-SIRST, our T2Det achieves competitive performance of 85.15%, 90.66% mAP, and 99.28 Pd, respectively, without unnecessary extra features.